spb/wp44_uqo

1{"work_id": 4415878254, "title": "DFCA: Decentralized Federated Clustering Algorithm", "abstract": "Clustered Federated Learning has emerged as an effective approach for handling heterogeneous data across clients by partitioning them into clusters with similar or identical data distributions. However, most existing methods, including the Iterative Federated Clustering Algorithm (IFCA), rely on a central server to coordinate model updates, typically requiring stable connectivity, synchronous communication rounds, and global aggregation of client models. These assumptions are difficult to satisfy in decentralized and heterogeneous environments, where clients may only have limited, local communication with a small subset of peers. As a result, such methods create a bottleneck and a single point of failure, limiting their applicability in realistic decentralized learning settings. This limitation is particularly severe in Internet of Things settings, where large numbers of resource-constrained devices, intermittent or sparse connectivity, and dynamic participation make reliance on a central server impractical. In this work, we introduce the Decentralized Federated Clustering Algorithm (DFCA), a fully decentralized clustered federated learning algorithm that enables clients to collaboratively train cluster-specific models without central coordination. DFCA uses a sequential running average to aggregate models from neighbors as updates arrive, providing a communication-efficient alternative to batch aggregation while maintaining clustering performance. Our experiments on various datasets demonstrate that DFCA outperforms other decentralized algorithms and performs comparably to centralized IFCA, even under sparse connectivity, highlighting its robustness and practicality for dynamic real-world decentralized networks."}2{"work_id": 3123868249, "title": "Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed through Cryptocurrencies?", "abstract": "Cryptocurrencies are among the largest unregulated markets in the world. We find that approximately one-quarter of bitcoin users are involved in illegal activity. We estimate that around $76 billion of illegal activity per year involve bitcoin (46% of bitcoin transactions), which is close to the scale of the U.S. and European markets for illegal drugs. The illegal share of bitcoin activity declines with mainstream interest in bitcoin and with the emergence of more opaque cryptocurrencies. The techniques developed in this paper have applications in cryptocurrency surveillance. Our findings suggest that cryptocurrencies are transforming the black markets by enabling \u201cblack e-commerce.\u201d Received June 1, 2017; editorial decision December 8, 2018 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online."}3{"work_id": 7155564432, "title": "Privacy-Preserving Anomaly Detection in Cloud Services using Hierarchical Federated Learning with Differential Privacy", "abstract": "Identifying abnormal behaviors plays a vital role in ensuring cloud infrastructure remains secure and operationally stable. Conventional methods that aggregate data at a single location create substantial privacy concerns, especially within shared cloud platforms hosting multiple organizations with confidential operational information. This paper proposes HierFedDP, a hierarchical federated learning framework integrated with a two-stage differential privacy mechanism for privacy-preserving anomaly detection in cloud services. Our approach employs a three-tier architecture consisting of local clients, edge servers, and a central cloud server, where clients apply local differential privacy to their updates before transmission. We introduce an edge aggregation frequency parameter that enables edge servers to perform multiple local aggregation rounds before communicating with the central cloud. Experiments on the CICIDS2017 dataset demonstrate that HierFedDP achieves detection performance comparable to standard local differential privacy approaches while reducing wide-area network (WAN) communication overhead by 49%. This significant communication reduction, achieved without sacrificing privacy guarantees or detection accuracy, makes HierFedDP particularly suitable for bandwidth-constrained, geo-distributed cloud deployments."}4{"work_id": 7104465905, "title": "Re-weighted softmax cross-entropy to control forgetting in federated learning", "abstract": "Dans l\u2019apprentissage f\u00e9d\u00e9r\u00e9, un mod\u00e8le global est appris en agr\u00e9geant les mises \u00e0 jour du mod\u00e8le calcul\u00e9es \u00e0 partir d\u2019un ensemble de n\u0153uds clients, un d\u00e9fi cl\u00e9 dans ce domaine est l\u2019h\u00e9t\u00e9rog\u00e9n\u00e9it\u00e9 des donn\u00e9es entre les clients qui d\u00e9grade les performances du mod\u00e8le. Les algorithmes d\u2019apprentissage f\u00e9d\u00e9r\u00e9 standard effectuent plusieurs \u00e9tapes de gradient avant de synchroniser le mod\u00e8le, ce qui peut amener les clients \u00e0 minimiser exag\u00e9r\u00e9ment leur propre objectif local et \u00e0 s\u2019\u00e9carter de la solution globale. Nous d\u00e9montrons que dans un tel contexte, les mod\u00e8les de clients individuels subissent un oubli catastrophique par rapport aux donn\u00e9es d\u2019autres clients et nous proposons une approche simple mais efficace qui modifie l\u2019objectif d\u2019entropie crois\u00e9e sur une base par client en repond\u00e9rant le softmax de les logits avant de calculer la perte. Cette approche prot\u00e8ge les classes en dehors de l\u2019ensemble d\u2019\u00e9tiquettes d\u2019un client d\u2019un changement de repr\u00e9sentation brutal. Gr\u00e2ce \u00e0 une \u00e9valuation empirique approfondie, nous d\u00e9montrons que notre approche peut att\u00e9nuer ce probl\u00e8me, en apportant une am\u00e9lioration continue aux algorithmes d\u2019apprentissage f\u00e9d\u00e9r\u00e9 standard. Cette approche est particuli\u00e8rement avantageux dans les contextes d\u2019apprentissage f\u00e9d\u00e9r\u00e9 difficiles les plus \u00e9troitement align\u00e9s sur les sc\u00e9narios du monde r\u00e9el o\u00f9 l\u2019h\u00e9t\u00e9rog\u00e9n\u00e9it\u00e9 des donn\u00e9es est \u00e9lev\u00e9e et la participation des clients \u00e0 chaque cycle est faible. Nous \u00e9tudions \u00e9galement les effets de l\u2019utilisation de la normalisation par lots et de la normalisation de groupe avec notre m\u00e9thode et constatons que la normalisation par lots, qui \u00e9tait auparavant consid\u00e9r\u00e9e comme pr\u00e9judiciable \u00e0 l\u2019apprentissage f\u00e9d\u00e9r\u00e9, fonctionne exceptionnellement bien avec notre softmax repond\u00e9r\u00e9, remettant en question certaines hypoth\u00e8ses ant\u00e9rieures sur la normalisation dans un syst\u00e8me f\u00e9d\u00e9r\u00e9"}5{"work_id": 4392771733, "title": "S\u00e9curisation des Algorithmes d'Apprentissage Automatique", "abstract": "Deep Neural Networks (DNNs) have seen significant advances in recent years and are nowadays widely used in a variety of applications. When it comes to safety-critical systems, developing methods and tools to make these algorithms reliable, particularly for non-specialists who may treat them as \u201cblack boxes\u201d with no further checks, constitutes a core challenge. This thesis aims to investigate various methods that can enable the safe use of these technologies.In the first part, we tackle the problem of identifying whether the prediction of a DNN classifier should (or should not) be trusted so that, consequently, it would be possible to accept or reject it. In this regard, we propose a new detector whichapproximates the most powerful (Oracle) discriminator based on the probability of classification error with respect to the true class posterior probability.Two scenarios are investigated: Totally Black Box (TBB), where only the soft-predictionsare available, and Partially Black Box (PBB) where gradient-propagation to perform input pre-processing is allowed. The proposed detector can be applied to any pre-trained model. It does not require prior information about the underlying dataset and is as simple as the simplest available methods in the literature.We address in the second part the problem of simultaneous adversarial example detection. The detection methods are generally validated by assuming a single implicitly known attack strategy, which does not necessarily account for real-life threats. Indeed, this can lead to an overoptimistic assessment of the detectors\u2019 performance andmay induce some bias in comparing competing detection schemes. We propose a novel multi-armed framework for evaluating detectors based on several attack strategies to overcome this limitation. Among them, we make use of three new objectives to generate attacks. The proposed performance metric is based on the worst-case scenario: detection is successful if and only if all different attacks are correctly recognized. Moreover, following this setting, we formally derive a simple yet effective method to aggregate the decisions of multiple trained detectors, possibly provided by a third party. While every single detector tends to underperform or fail at detecting types of attack that it has never seen at training time, our framework successfully aggregates the knowledge of the available detectors to guarantee a robust detection algorithm. The proposed method has many advantages: it is simple as it does not require further training of the given detectors; it is modular, allowing existing (and future) methods to be merged into a single one; it is general since it can simultaneously recognize adversarial examples created according to different algorithms and training (loss) objectives."}6{"work_id": 4386476569, "title": "Blockchain-based data governance for privacy-preserving in multi-stakeholder settings", "abstract": "Em sistemas envolvendo m\u00faltiplas partes interessadas, como o setor da sa\u00fade, internet das coisas e o gerenciamento da cadeia de suprimentos, h\u00e1 uma gera\u00e7\u00e3o, troca e compartilhamento frequente de dados.Como resultado, os propriet\u00e1rios dos dados geralmente precisam controlar e preservar a privacidade de suas informa\u00e7\u00f5es, enquanto os consumidores dos dados exigem m\u00e9todos para determinar as origens e os criadores dos registros.Esses conflitos exigem solu\u00e7\u00f5es de governan\u00e7a que garantam a proveni\u00eancia dos dados, prote\u00e7\u00e3o da privacidade,"}7{"work_id": 4214667959, "title": "A survey on blockchain technology and its security", "abstract": "Blockchain is a technology that has desirable features of decentralization, autonomy, integrity, immutability, verification, fault-tolerance, anonymity, auditability, and transparency. In this paper, we first carry out a deeper survey about blockchain technology, especially its history, consensus algorithms' quantitative comparisons, details of cryptography in terms of public key cryptography, Zero-Knowledge Proofs, and hash functions used in the blockchain, and the comprehensive list of blockchain applications. Further, the security of blockchain itself is a focus in this paper. In particular, we assess the blockchain security from risk analysis to derive comprehensive blockchain security risk categories, analyze the real attacks and bugs against blockchain, and summarize the recently developed security measures on blockchain. Finally, the challenges and research trends are presented to achieve more scalable and securer blockchain systems for the massive deployments."}8{"work_id": 2624307925, "title": "A Survey on Security and Privacy Issues of Bitcoin", "abstract": "Bitcoin is a popular cryptocurrency that records all transactions in a distributed append-only public ledger called blockchain. The security of Bitcoin heavily relies on the incentive-compatible proof-of-work (PoW) based distributed consensus protocol, which is run by the network nodes called miners. In exchange for the incentive, the miners are expected to maintain the blockchain honestly. Since its launch in 2009, Bitcoin economy has grown at an enormous rate, and it is now worth about 150 billions of dollars. This exponential growth in the market value of bitcoins motivate adversaries to exploit weaknesses for profit, and researchers to discover new vulnerabilities in the system, propose countermeasures, and predict upcoming trends. In this paper, we present a systematic survey that covers the security and privacy aspects of Bitcoin. We start by giving an overview of the Bitcoin system and its major components along with their functionality and interactions within the system. We review the existing vulnerabilities in Bitcoin and its major underlying technologies such as blockchain and PoW-based consensus protocol. These vulnerabilities lead to the execution of various security threats to the standard functionality of Bitcoin. We then investigate the feasibility and robustness of the state-of-the-art security solutions. Additionally, we discuss the current anonymity considerations in Bitcoin and the privacy-related threats to Bitcoin users along with the analysis of the existing privacy-preserving solutions. Finally, we summarize the critical open challenges, and we suggest directions for future research towards provisioning stringent security and privacy solutions for Bitcoin."}9{"work_id": 2898069736, "title": "Blockchain technology and its relationships to sustainable supply chain management", "abstract": "Globalisation of supply chains makes their management and control more difficult. Blockchain technology, as a distributed digital ledger technology which ensures transparency, traceability, and security, is showing promise for easing some global supply chain management problems. In this paper, blockchain technology and smart contracts are critically examined with potential application to supply chain management. Local and global government, community, and consumer pressures to meet sustainability goals prompt us to further investigate how blockchain can address and aid supply chain sustainability. Part of this critical examination is how blockchains, a potentially disruptive technology that is early in its evolution, can overcome many potential barriers. Four blockchain technology adoption barriers categories are introduced; inter-organisational, intra-organisational, technical, and external barriers. True blockchain-led transformation of business and supply chain is still in progress and in its early stages; we propose future research propositions and directions that can provide insights into overcoming barriers and adoption of blockchain technology for supply chain management."}10{"work_id": 7129030749, "title": "Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions", "abstract": "The Model Context Protocol (MCP) is an emerging open standard that defines a unified, bi-directional communication and dynamic discovery protocol between AI models and external tools or resources, aiming to enhance interoperability and reduce fragmentation across diverse systems. This paper conducts a systematic study of MCP from both architectural and security perspectives. We first define the full lifecycle of an MCP server, comprising four phases (creation, deployment, operation, and maintenance), further decomposed into 16 key activities that capture its functional evolution. Building on this lifecycle analysis, we construct a comprehensive threat taxonomy that categorizes security and privacy risks across four major attacker types: malicious developers, external attackers, malicious users, and security flaws, encompassing 16 distinct threat scenarios. To validate these risks, we develop and analyze real-world case studies that demonstrate concrete attack surfaces and vulnerability manifestations within MCP implementations. Based on these findings, the paper proposes a set of fine-grained, actionable security safeguards tailored to each lifecycle phase and threat category, offering practical guidance for secure MCP adoption. We also analyze the current MCP landscape, covering industry adoption, integration patterns, and supporting tools, to identify its technological strengths as well as existing limitations that constrain broader deployment. Finally, we outline future research and development directions aimed at strengthening MCP\u2019s standardization, trust boundaries, and sustainable growth within the evolving ecosystem of tool-augmented AI systems. All collected data and implementation examples are publicly available at https://github.com/security-pride/MCP_Landscape ."}11{"work_id": 1516024962, "title": "Energy Efficient Adaptive Reed-Solomon Decoding System", "abstract": "This work presents an energy efficient adaptive error correction system utilizing the Reed-Solomon errors-and-erasures algorithm, targeted to an Altera Stratix FPGA device. The system adapts to changing channel conditions by reconfiguring the system with different decoders to allow for the lowest possible energy consumption rate that the current channel conditions will allow.\\nA series of energy saving optimizations were applied to a set of previous designs, resulting in a reduction in the energy required to decode a megabit of data of more than 70%. In addition, a new channel model was used to assess the effects of differing reconfiguration rates on codeword error rate, energy consumption, and decoding speed."}12{"work_id": 7134847443, "title": "3D Non-Degenerate Hyperchaos: Design, Analysis, and Application in Image Encryption", "abstract": "The widespread adoption of consumer electronics (CE), particularly in smart home ecosystems, has led to the proliferation of intelligent devices such as smart cameras, video doorbells, and smart door locks. While these devices enhance convenience and automation, they generate massive amounts of visual data containing sensitive personal information, thereby increasing the risk of privacy leakage and challenging traditional encryption methods in consumer IoT networks. To address this issue, a cryptanalysis-driven image encryption scheme via a chaos-based 3D S-box and genetic code is proposed, specifically designed to safeguard visual privacy in consumer applications. First, a novel dynamical architecture, formally termed 3D Non-degenerate Hyperchaos (3D-NDHC), is constructed to serve as a robust entropy source. Explicitly defined as a globally bounded volume-expanding hyperchaotic system, 3D-NDHC distinguishes itself from traditional dissipative maps by exhibiting global robust hyperchaos and scalable complexity, where the Lyapunov exponents grow logarithmically with control parameters, ensuring high unpredictability. Next, a dynamically generated three-dimensional S-box driven by these chaotic sequences is introduced to perform efficient image permutation. Subsequently, a genetic code method enhanced by cryptanalysis is applied to confuse the permuted image matrix, specifically addressing the security vulnerabilities often inherent in lightweight encryption algorithms. Finally, under the control of pseudo-chaotic sequences, a lightweight wave diffusion process is employed to spread the pixel values and produce the final ciphertext image. In contrast to conventional algorithms that rely on standard encryption techniques, the proposed approach integrates a chaos-driven 3D S-box with a cryptanalysis-enhanced genetic code scheme. This combination introduces a high level of confusion and diffusion, significantly increasing the difficulty for attackers attempting to illegally decrypt sensitive images from smart devices. Experimental results and security analyses demonstrate that the scheme effectively resists various cryptographic attacks and outperforms several recently proposed image encryption algorithms, offering a robust solution for protecting digital image privacy in smart home environments."}13{"work_id": 3041344359, "title": "Machine Learning for anomaly detection in IoT networks :Malware analysis on the IoT-23 data set", "abstract": "The Internet of Things is one of the newer developments\r\nin the domain of the Internet. It is defined as a network\r\nof connected devices and sensors, both physical and digital, that generate and exchange large amounts of data\r\nwithout the need for human intervention. As a result\r\nof eliminating the need for human operators, the IoT\r\n(Internet of Things) can process more data than ever\r\nbefore faster and more efficient.\r\nThis paper focuses on the security aspect of IoT networks by investigating the usability of machine learning\r\nalgorithms in the detection of anomalies found within\r\nthe data of such networks. It examines ML algorithms\r\nthat are successfully utilized in relatively similar situations and compares using a number of parameters and\r\nmethods.\r\nThis paper implements the following algorithms: Random Forest (RF), Naive Bayes (NB), Multi Layer\r\nPerceptron (MLP), a variant of the Artificial Neural\r\nNetwork class of algorithms, Support Vector Machine\r\n(SVM) and AdaBoost (ADA). The best results were\r\nachieved by the Random Forest algorithm, with a accuracy or 99.5%."}14{"work_id": 4407111324, "title": "Exploring the effects of artificial intelligence on student and academic well-being in higher education: a mini-review", "abstract": "The increasing use of artificial intelligence (AI) in higher education is reshaping how students engage with their academic and personal lives. However, the impact of AI on students' well-being remains underexplored. This mini-review synthesizes current literature to assess how AI affects student well-being, focusing on mental health, social interactions, and academic experiences. While AI offers benefits such as personalized learning, mental health support, and improved communication efficiency, it also raises concerns regarding digital fatigue, loneliness, technostress, and reduced face-to-face interactions. Over-reliance on AI may diminish interpersonal skills and emotional intelligence, leading to social isolation and anxiety. Furthermore, issues such as data privacy and job displacement emerge as AI technologies permeate educational environments. The review highlights the need for balanced AI integration that supports both academic success and student well-being, advocating for further empirical studies to comprehensively understand these dynamics. As AI becomes more embedded in education, it is crucial to develop strategies that mitigate its negative effects while promoting holistic well-being among students."}15{"work_id": 4254706618, "title": "Status report on the second round of the NIST post-quantum cryptography standardization process", "abstract": "The Information Technology Laboratory (ITL) at the National Institute of Standards and Technology (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the Nation's measurement and standards infrastructure.ITL develops tests, test methods, reference data, proof of concept implementations, and technical analyses to advance the development and productive use of information technology.ITL's responsibilities include the development of management, administrative, technical, and physical standards and guidelines for the cost-effective security and privacy of other than national security-related information in federal information systems."}16{"work_id": 3041977451, "title": "Analyzing adoption of contact tracing apps using UTAUT", "abstract": "Contact tracing apps have been a highly debated topic during the COVID-19 epidemic. Recently released COVID-19 contact tracing apps have been lagging behind in terms of user adoption. As user adoption is crucial to the effectiveness of contact tracing apps, there needs to be a deeper investigation into possible influences on the user adoption of such apps. Therefore, this research will propose a modified version of the Unified Theory of Acceptance and Use of Technology (UTAUT) founded on a systematic literature review. A survey has been conducted with 134 participants based on this modified model. Performance expectancy and perceived credibility have been determined to have a significant impact on the intention to use a contract tracing app. Apart from providing receiving notifications about possible infections, current contract tracing apps appear to not provide a clear benefit to the user and are perceived as somewhat unsafe and privacy-invading. Furthermore, contact tracing apps might turn out to be a failure, as this research finds a low intention to use such apps. This research contributes to understanding user adoption of contract tracing apps and gives insights into possible improvements regarding the development and approach of such apps."}17{"work_id": 4406028284, "title": "A Novel Dynamic Hill Cipher and Its Applications on Medical IoT", "abstract": "Cryptography is one of the most important areas in information security. Cryptography ensures secure communication and data privacy, and it is increasingly being applied in healthcare and related fields. As an important classical cryptographic method, the Hill cipher has always been closely studied by experts and scholars. In order to enhance the security of the conventional Hill cipher (CHC), a novel dynamic Hill cipher (NDHC) is proposed in this work. The proposed NDHC not only replaces the static key matrix of the CHC with a time-varying dynamic key matrix (TVDKM) to change the image pixel values over time t, but also uses the Logistic chaos sequence scrambling the image pixel positions, which greatly enhances the security of the CHC. However, how to effectively obtain the dynamic inversion key matrix (DIKM) of the TVDKM becomes an urgent issue in the NDHC decryption. In order to quickly find the DIKM, a fixed-time convergence fuzzy Zeroing neural network (FTCF-ZNN) model is constructed, and the convergence and robustness of the FTCF-ZNN model for solving the DIKM are verified through theoretical analysis and comparative experimental results. Moreover, the effectiveness and security of the proposed NDHC for medical images encryption and decryption are also validated by experiments."}18{"work_id": 3162358200, "title": "Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges", "abstract": "The Non-Fungible Token (NFT) market is mushrooming in recent years. The concept of NFT originally comes from a token standard of Ethereum, aiming to distinguish each token with distinguishable signs. This type of token can be bound with virtual/digital properties as their unique identifications. With NFTs, all marked properties can be freely traded with customized values according to their ages, rarity, liquidity, etc. It has greatly stimulated the prosperity of the decentralized application (DApp) market. At the time of writing (May 2021), the total money used on completed NFT sales has reached $34,530,649.86$ USD. The thousandfold return on its increasing market draws huge attention worldwide. However, the development of the NFT ecosystem is still in its early stage, and the technologies of NFTs are pre-mature. Newcomers may get lost in their frenetic evolution due to the lack of systematic summaries. In this technical report, we explore the NFT ecosystems in several aspects. We start with an overview of state-of-the-art NFT solutions, then provide their technical components, protocols, standards, and desired proprieties. Afterward, we give a security evolution, with discussions on the perspectives of their design models, opportunities, and challenges. To the best of our knowledge, this is the first systematic study on the current NFT ecosystems."}19{"work_id": 2795085730, "title": "Blockchain: A Panacea for Healthcare Cloud-Based Data Security and Privacy?", "abstract": "One particular trend observed in healthcare is the progressive shift of data and services to the cloud, partly due to convenience (e.g. availability of complete patient medical history in real-time) and savings (e.g. economics of healthcare data management). There are, however, limitations to using conventional cryptographic primitives and access control models to address security and privacy concerns in an increasingly cloud-based environment. In this paper, we study the potential to use the Blockchain technology to protect healthcare data hosted within the cloud. We also describe the practical challenges of such a proposition and further research that is required."}20{"work_id": 4408302972, "title": "The Impact of Artificial Intelligence (AI) on Students\u2019 Academic Development", "abstract": "The integration of Artificial Intelligence (AI) in education has transformed academic learning, offering both opportunities and challenges for students\u2019 development. This study investigates the impact of AI technologies on students\u2019 learning processes and academic performance, with a focus on their perceptions and the challenges associated with AI adoption. Conducted at the National University of Science and Technology POLITEHNICA Bucharest, this research involved second-year students who had direct experience with AI-enhanced learning environments. Using purposive sampling, 85 participants were selected to ensure relevance. Data were collected through a structured questionnaire comprising 11 items as follows: seven closed-ended questions assessing perceptions, usage, and the effectiveness of AI tools; and four open-ended questions exploring experiences, expectations, and concerns. Quantitative data were analyzed using frequency and percentage calculations, while qualitative responses were subjected to thematic analysis, incorporating both vertical (individual responses) and horizontal (cross-dataset) approaches to ensure comprehensive theme identification. The findings reveal that AI offers significant benefits, including personalized learning, improved academic outcomes, and enhanced student engagement. However, challenges such as over-reliance on AI, diminished critical thinking skills, data privacy risks, and academic dishonesty were also identified. The study underscores the necessity of a structured framework for AI integration, supported by ethical guidelines, to maximize benefits while mitigating risks. In conclusion, while AI holds immense potential to enhance learning efficiency and academic performance, its successful implementation requires addressing concerns related to accuracy, cognitive disengagement, and ethical implications. A balanced approach is essential to ensure equitable, effective, and responsible learning experiences in AI-enhanced educational environments."}21{"work_id": 3133814152, "title": "Towards Personalized Federated Learning", "abstract": "In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI research, there has been growing awareness and concerns of data privacy. Recent significant developments in the data regulation landscape have prompted a seismic shift in interest toward privacy-preserving AI. This has contributed to the popularity of Federated Learning (FL), the leading paradigm for the training of machine learning models on data silos in a privacy-preserving manner. In this survey, we explore the domain of personalized FL (PFL) to address the fundamental challenges of FL on heterogeneous data, a universal characteristic inherent in all real-world datasets. We analyze the key motivations for PFL and present a unique taxonomy of PFL techniques categorized according to the key challenges and personalization strategies in PFL. We highlight their key ideas, challenges, opportunities, and envision promising future trajectories of research toward a new PFL architectural design, realistic PFL benchmarking, and trustworthy PFL approaches."}22{"work_id": 4220990555, "title": "Robust federated learning with attack-adaptive aggregation", "abstract": "HKUST Electronic Theses Robust federated learning with attack-adaptive aggregation by Wan Ching Pui thesis 2021 1 online resource (x, 50 pages) : illustrations (chiefly color) In this thesis, we discuss a data-driven approach to defend adversarial attacks in federated learning.\u2026Read more \u203a"}23{"work_id": 4414891672, "title": "Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications", "abstract": "Differential privacy (DP) is a prominent technique for protecting sensitive patient data in medical deep learning (DL), yet deploying it without compromising clinical utility or equity remains challenging. This scoping review synthesizes applications of DP in medical DL across centralized and federated settings. A structured search identified 74 eligible studies published through March 2025. Across modalities and tasks, DP, especially via DP-SGD, can maintain clinically acceptable performance under moderate privacy budgets (\u03f5 \u2248 10), particularly in imaging. However, strict privacy (\u03f5 \u2248 1) often leads to substantial accuracy loss, with amplified degradation in smaller or heterogeneous datasets. Only a minority of studies evaluate fairness, and several report that DP can widen subgroup performance gaps. Beyond DP-SGD, alternative mechanisms, including generative modeling, local DP, and hybrid federated designs, are emerging, but reporting of privacy parameters remains inconsistent. We identify key gaps in fairness auditing and standardization, and outline priorities for equitable, clinically robust privacy-preserving DL."}24{"work_id": 3016632787, "title": "Federated Learning With Differential Privacy: Algorithms and Performance Analysis", "abstract": "Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients\u2019 private data from being exposed to adversaries. Nevertheless, private information can still be divulged by analyzing uploaded parameters from clients, e.g., weights trained in deep neural networks. In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noise is added to parameters at the clients\u2019 side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noise. Then we develop a theoretical convergence bound on the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) there is a tradeoff between convergence performance and privacy protection levels, i.e., better convergence performance leads to a lower protection level; 2) given a fixed privacy protection level, increasing the number$N$of overall clients participating in FL can improve the convergence performance; and 3) there is an optimal number aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a$K$-client random scheduling strategy, where$K$($1\\leq K< N$) clients are randomly selected from the$N$overall clients to participate in each aggregation. We also develop a corresponding convergence bound for the loss function in this case and the$K$-client random scheduling strategy also retains the above three properties. Moreover, we find that there is an optimal$K$that achieves the best convergence performance at a fixed privacy level. Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the design of various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels."}25{"work_id": 7135198685, "title": "Breaking interprovincial data silos: how federated learning can unlock Canada's public health potential", "abstract": "Abstract This study introduces the first dual-enabler framework that jointly integrates federated learning (FL) with artificial intelligence (AI) governance to enable privacy-preserving, interoperable collaboration across jurisdictions. Unlike traditional FL approaches, the proposed framework embeds governance mechanisms such as differential privacy (DP), consent enforcement and fairness auditing into the model lifecycle, ensuring secure, compliant and equitable deployment. These conditions result in siloed datasets, impeding national-level advancements in diagnostics, public health and personalized medicine. The framework\u2019s novelty lies in its co-evolutionary design, where technical and governance layers iteratively inform one another to align with Canada\u2019s federated healthcare architecture. We evaluate this framework via three simulations: cancer detection, pandemic prediction, and rare disease analytics and a real-world test using the PathMNIST dataset. Each case incorporates embedded governance mechanisms such as DP, consent enforcement and fairness auditing. Results show that FL combined with governance structures improves equity, deployment speed and compliance. For instance, in synthetic-data simulations, FL improved recall for underrepresented phenotypes by 18%, reduced compliance-violation flags by 35% and accelerated deployment. In the PathMNIST benchmark with 107 000 labelled medical images, our federated model achieved 91.3% accuracy under a strict privacy budget. Together, these results demonstrate a clear and scalable pathway towards ethical, privacy-preserving and governance-aligned AI for Canada\u2019s healthcare ecosystem."}26{"work_id": 2781723315, "title": "Spectre Attacks: Exploiting Speculative Execution", "abstract": "Modern processors use branch prediction and speculative execution to maximize performance. For example, if the destination of a branch depends on a memory value that is in the process of being read, CPUs will try to guess the destination and attempt to execute ahead. When the memory value finally arrives, the CPU either discards or commits the speculative computation. Speculative logic is unfaithful in how it executes, can access the victim's memory and registers, and can perform operations with measurable side effects. Spectre attacks involve inducing a victim to speculatively perform operations that would not occur during correct program execution and which leak the victim's confidential information via a side channel to the adversary. This paper describes practical attacks that combine methodology from side channel attacks, fault attacks, and return-oriented programming that can read arbitrary memory from the victim's process. More broadly, the paper shows that speculative execution implementations violate the security assumptions underpinning numerous software security mechanisms, including operating system process separation, containerization, just-in-time (JIT) compilation, and countermeasures to cache timing and side-channel attacks. These attacks represent a serious threat to actual systems since vulnerable speculative execution capabilities are found in microprocessors from Intel, AMD, and ARM that are used in billions of devices. While makeshift processor-specific countermeasures are possible in some cases, sound solutions will require fixes to processor designs as well as updates to instruction set architectures (ISAs) to give hardware architects and software developers a common understanding as to what computation state CPU implementations are (and are not) permitted to leak."}27{"work_id": 4392353733, "title": "A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly", "abstract": "Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized natural language understanding and generation. They possess deep language comprehension, human-like text generation capabilities, contextual awareness, and robust problem-solving skills, making them invaluable in various domains (e.g., search engines, customer support, translation). In the meantime, LLMs have also gained traction in the security community, revealing security vulnerabilities and showcasing their potential in security-related tasks. This paper explores the intersection of LLMs with security and privacy. Specifically, we investigate how LLMs positively impact security and privacy, potential risks and threats associated with their use, and inherent vulnerabilities within LLMs. Through a comprehensive literature review, the paper categorizes the papers into \u201cThe Good\u201d (beneficial LLM applications), \u201cThe Bad\u201d (offensive applications), and \u201cThe Ugly\u201d (vulnerabilities of LLMs and their defenses). We have some interesting findings. For example, LLMs have proven to enhance code security (code vulnerability detection) and data privacy (data confidentiality protection), outperforming traditional methods. However, they can also be harnessed for various attacks (particularly user-level attacks) due to their human-like reasoning abilities. We have identified areas that require further research efforts. For example, Research on model and parameter extraction attacks is limited and often theoretical, hindered by LLM parameter scale and confidentiality. Safe instruction tuning, a recent development, requires more exploration. We hope that our work can shed light on the LLMs\u2019 potential to both bolster and jeopardize cybersecurity."}28{"work_id": 2309315845, "title": "Human-centric training and assessment for cyber situation awareness", "abstract": "\"Cyber attacks have been increasing significantly in both number and complexity, prompting the need for better training of cyber defense analysts. One issue with existing cyber security training is that it relies mostly on lecture-style instructions without much hand- on experience. Thus, need a training solution that provides a realistic, human-in-the-loop environment for cyber analysts to explore, collaborate, and interact for effective learning. Situation Awareness (SA) means the comprehension and perception of environmental elements with respect to time and space. Cyber Situation Awareness (CSA) is SA extended to the cyber domain. During CSA, the cyber analysts need to understand the meaning of the observations and be able to project the impact of the observations to the system. In this proposal, we describe a Cyber Situation Awareness training and assessment system with the purpose of teaching and measuring individual and team cyber situation awareness within the cyber defense context, as well as incorporating various technologies to enhance the cyber analysts\u2019 learning process. To conduct effective cyber security training, it becomes essential to design realistic exercise lesson plans. Accurate identification of experts\u2019 cognitive processes through Cognitive Task Analysis can be adapted into training materials to teach novice cyber analysts (or trainees in this proposal) how to think and act like an expert during defense. In order to solve the information overload challenge faced by trainees, we identify and design watch list statistics, which allows trainees to tailor their own watch list statistics and triggering thresh- old conditions in order to recognize cyber attacks faster. The speed with which a trainee can recognize, analyze, and respond to attacks is critical as it will limit the damage and lower the cost of recovery. Therefore, we evaluate trainees\u2019 performance based on their response time comparing with estimated attack ground truth timeline. We also devise scoring algorithms to calculate trainees\u2019 performance scores according to the weighted functions combing all performance metrics. Then, as training is an iterative process, the assessment component not only assesses the knowledge gained by the cyber analysts, but also adjusts the difficulty of training lessons accordingly based on trainees\u2019 performance. Nevertheless, quantifying difficulty level of training lesson scenario is an important but difficult task. While standard techniques exist for measuring the relative difficulty to exploit individual vulnerability, it is challenging to answer the fundamental question whether one scenario containing several vulnerabilities is more difficult than another one. Based on causal relationships between vulnerabilities in attack graph, we apply Bayesian Reasoning to aggregate individual vulnerabilities into a probabilistic value representing the attackers\u2019 success likelihood to achieve the attack goal. Based on the quantified probability of achieving attack goal, the lesson\u2019s difficulty-level is categorized accordingly. Furthermore, complex and dynamically changing task such as cyber defense often requires the effective coordination of a team of cyber analysts. Cyber analysts need to work collaboratively as a team at different levels and different parts of system. Each team member collects data, generates its own awareness for the cyber situation, and shares with other team members to get the comprehensive understanding of the overall situation for the decision making purpose. Since each team member may have his/her own personal expertise knowledge, experience, and opinions, it is hard for the entire team to make consensus decision when having conflicting judgments. Considering human cyber analysts tend to use ambiguous linguistic language to express their own cyber situation awareness during the team discussion process, we design a fuzzy set based method to facilitate cyber analysts to quantify their thoughts and make consensus decision that is most acceptable by the entire team. Finally, we investigate real-time purpose sensors based information fusion for Advanced Persistent Threats (APTs) detection. As human cyber analysts have to examine huge amount of data such as system logs, configuration files, traffic logs, IDS log, and audit logs in order to identify potential threats. Thus, they would be soon overwhelmed by tremendous xvi data and forced to ignore potentially significant evidences introducing errors in the detection process. Furthermore, cyber security attack and anomaly detection techniques suffer from their reliance on known malicious signatures or unusual conditions that warrant further investigation. The use of signature-based detection cannot effectively eliminate false negatives when dealing with Advanced Persistent Threats, since the financial resources and time available to APTs allows the use of previously unknown \u2018zero-day\u2019 attack vectors. The designed information fusion system reduces an operator burden to handle false positives and reduces time to detection while using noisy/high false positive inputs. We design and develop hostand network- based purpose sensors and places them within the network and individual hosts to provide real-time purpose and correlation inputs and then use this information combined with network-specific knowledge to create a dynamic set of event threads that, when touched by a given alert received from traditional intrusion detection systems (IDSs) such as Snort and OSSEC, allows the immediate identification of the context surrounding the alert and thus the automatic calculation of the alert\u2019s legitimacy and severity.\""}29{"work_id": 1550000763, "title": "Crying Wolf: An Empirical Study of SSL Warning Effectiveness", "abstract": "Web users are shown an invalid certificate warning when their browser cannot validate the identity of the websites they are visiting. While these warnings often appear in benign situations, they can also signal a man-in-the-middle attack. We conducted a survey of over 400 Internet users to examine their reactions to and understanding of current SSL warnings. We then designed two new warnings using warnings science principles and lessons learned from the survey. We evaluated warnings used in three popular web browsers and our two warnings in a 100- participant, between-subjects laboratory study. Our warnings performed significantly better than existing warnings, but far too many participants exhibited dangerous behavior in all warning conditions. Our results suggest that, while warnings can be improved, a better approach may be to minimize the use of SSL warnings altogether by blocking users from making unsafe connections and eliminating warnings in benign situations."}30{"work_id": 2606010969, "title": "Blockchains for Business Process Management - Challenges and Opportunities", "abstract": "Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM."}31{"work_id": 44936433, "title": "How to play any mental game, or a completeness theorem for protocols with honest majority", "abstract": "research-article Share on How to play any mental game, or a completeness theorem for protocols with honest majority Authors: Oded Goldreich Technion TechnionView Profile , Silvio Micali MIT MITView Profile , Avi Wigderson Hebrew University Hebrew UniversityView Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 307\u2013328https://doi.org/10.1145/3335741.3335755Published:04 October 2019Publication History 57citation435DownloadsMetricsTotal Citations57Total Downloads435Last 12 Months42Last 6 weeks11 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access"}32{"work_id": 3188417193, "title": "Machine learning for offensive cyber operations", "abstract": "Cybersecurity with machine learning has received widespread attention in education, research, and innovation in both the private and public sectors. Unfortunately, while essential for strong cyber security, offensive cyber operations with machine learning have seen significantly less innovation, at least in open academic literature. This thesis's contribution to the field of offensive cyber operations with machine learning can naturally be divided into the following: 1. Algorithmic cryptanalysis with machine learning 2. SQL injection with machine learning The historical cipher Enigma's plugboard is shown to be susceptible to an attack powered by the machine learning technique Genetic Algorithms, being broken far faster than any earlier attack. Modern ciphers are naturally more robust than historical ciphers. The cryptographic algorithm ASCON is still secure, but the novel machine learning technique, The Phantom Gradient Attack, is shown to attack many of its subfunctions successfully. OWASP's top 10 had SQL injections as the number one web vulnerability in 2017; in 2021, it was number three. This thesis highlights the possibility of automated SQL injection exploitation and identification with reinforcement learning for accelerated penetration testing. The reinforcement learning agent can exploit all 5 SQL injection archetypes, distinguish between them, and determine whether or not the website is vulnerable."}33{"work_id": 4396670109, "title": "Advanced Digital Data Processing Using Cloud Cryptography", "abstract": "Cloud computing uses the internet instead of discs or memory. Computing services include servers, databases, networks, and programmes. The primary benefit of cloud computing is easy and cheap data backup and access from anywhere. Cloud storage doesn't store consumer data, raising safety concerns. Cloud backup and storage users may not know how data is transported. The user is unaware a third party is secretly accessing their data. For safety, we offer numerous encryption algorithms. This book chapter covered cryptography and cloud computing."}34{"work_id": 4286110439, "title": "\u00bbOhne die geht\u2019s nicht\u00ab Zur Rolle von Apple und Google bei der Corona-Warn-App", "abstract": "Die COVID-19-Pandemie ist eine gesellschaftliche Gesundheitskrise, in der erstmals weltweit in gro\u00dfem Stil populationsbasierte Technologien der Kontaktnachverfolgung (sog.Contact-Tracing-bzw. Pandemie-Apps) zur Pandemiebek\u00e4mpfung eingesetzt werden.Mit ihnen ist das Ziel verbunden, die Ausbreitung des Virus zu verhindern, indem Begegnungen aufgezeichnet und Warnungen \u00fcber m\u00f6gliche Risikokontakte verschickt werden.Hierzu \u00fcbersetzen Apps wie die von der Bundesregierung gemeinsam mit dem Robert-Koch-Institut (RKI) herausgegebene \u00bbCorona-Warn-App\u00ab (CWA) mithilfe des Smartphones Kontakte \u00fcber Bluetooth-Signale in Daten und berechnen mit diesen einen Score f\u00fcr das Risiko einer \u00dcbertragung (sog.\u00bbRisk-Score\u00ab).Dabei ist zu beachten, dass die Contact-Tracing-Apps aus jeweils nationalen Apps als \u00bbClients\u00ab auf dem jeweiligen mobilen Endger\u00e4t und den zentralen Servern bestehen.Diese interagieren \u00fcber die von Google und Apple bereitgestellte Schnittstelle, das Google Apple Exposure Notification Framework (GA-ENF), und erm\u00f6glichen so die bluetoothbasierte Kontaktnachverfolgung (Dix 2020, S. 780).Die gemeinsame Arbeit f\u00fcr die digitale Pandemiebek\u00e4mpfung der beiden Konkurrenzunternehmen Apple und Google stie\u00df weltweit auf Begeisterung und Verwunderung (Sharon 2020, S. 4).Zugleich lassen sich Google und Apple mit Philipp Staab (2019) als zwei Leitunternehmen des digitalen Kapitalismus begreifen, die sich durch ihr gro\u00dfes \u00f6konomisches Kapital auszeichnen und sich als Kontrolleure bzw.Betreiber der Smartphonebetriebssysteme Android und iOS als Metaplattformen etablieren konnten.In einer Stellungnahme zum Contact-Tracing betont Apple die Motivation f\u00fcr die Zusammenarbeit mit Google wie folgt:"}35{"work_id": 3018464563, "title": "FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare", "abstract": "With the rapid development of computing technology, wearable devices make it easy to get access to people's health information. Smart healthcare achieves great success by training machine learning models on a large quantity of user personal data. However, there are two critical challenges. First, user data often exist in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Second, the models trained on the cloud fail on personalization. In this article, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds relatively personalized models by transfer learning. Wearable activity recognition experiments and real Parkinson's disease auxiliary diagnosis application have evaluated that FedHealth is able to achieve accurate and personalized healthcare without compromising privacy and security. FedHealth is general and extensible in many healthcare applications."}36{"work_id": 2946416612, "title": "Inadvertently Making Cyber Criminals Rich: A Comprehensive Study of Cryptojacking Campaigns at Internet Scale", "abstract": "Cryptojacking, a phenomenon also known as drive-by cryptomining, involves stealing computing power from others to be used in illicit cryptomining. While first observed as host-based infections with low activity, the release of an efficient browser-based cryptomining application -- as introduced by Coinhive in 2017 -- has skyrocketed cryptojacking activity in recent years. This novel method of monetizing Web activity attracted both website owners and cybercriminals seeking new methods to profit from. Website owners installed a cryptominer on their domains, while cybercriminals deployed cryptominers in large campaigns spread over numerous domains. Several studies developed detection methods to identify these browser-based cryptominers on websites, but none of these studies focused on the extent and coordination of campaigns deployed by adversaries. Furthermore, the prevalence of cryptojacking on websites is not well estimated yet and the potentially largest attack vector -- a man-in-the-middle attack -- has never been researched before. In this thesis, we perform multiple large studies on cryptojacking to fill these gaps. After crawling a random sample of 49M domains, 20% of the Internet, we conclude that cryptojacking is present on 0.011% of all domains and that adult content is the most prevalent category of websites affected. We show that this percentage is significantly larger in the popular part of the Internet. This led to the conclusion that surveying solely domains listed in the Alexa Top 1M to estimate cryptojacking prevalence results in an overestimation of the problem. Furthermore, we show that infection rates on different Top Level Domains (TLDs) differ widely, as the Russian zone is home to a disproportionate number of cryptojacking domains, while other large TLDs -- such as .com -- show a significantly lower number of infections. In another crawl, we have identified 204 cryptojacking campaigns on websites, an order of magnitude more than previous work, which indicates that the extent of these campaigns is heavily underestimated. The results of the two crawls combined reveal that 48% of all cryptojacking activity on websites is organized. The identified campaigns ranged in sizes from only 5 to 987 websites and we discovered that cybercriminals have chosen third-party software -- such as WordPress and Drupal -- as their method of choice for spreading cryptojacking infections efficiently. With a novel method of using NetFlow data recorded in a Tier 1 network, we estimated the popularity of mining applications, which showed that while Coinhive has a larger installed base, CoinImp WebSocket proxies were digesting significantly more traffic in the second half of 2018. We have reported about a new attack vector that drastically overshadows all other cryptojacking activity. Through a firmware vulnerability in MikroTik routers, cybercriminals are able to rewrite outgoing user traffic and embed cryptomining code in every outgoing Web connection. Thus, every Web page visited by any user behind an infected router would mine to profit the adversaries. Based on the aforementioned NetFlow data, weekly third-party crawls and network telescope traffic, we were able to follow their activities over a period of 10 months. We report on the modus operandi and coordinating infrastructure of the perpetrators, which were during this period in control of up to 1.4M routers, which is approximately 70% of all MikroTik devices deployed in the world. During the peak of this attack, more than 440K routers were infected concurrently. We have discovered that half of the infected routers are patched within 18 days after compromise, but 30% of the infections last longer than 50 days. Additionally, we observed different levels of sophistication among adversaries, ranging from individual installations to campaigns involving large numbers of routers. The combination of datasets allowed us to link tens of seemingly different infections to one actor. Our analysis of cryptojacking with a focus on organized campaigns has shown that cybercriminals have successfully discovered a new method for monetary gain. With the discontinuation of Coinhive due to decreased Monero prices in March 2019, the cryptojacking landscape has changed enormously, and we are curious who will fill this power vacuum. As browser-based mining is not anywhere near as profitable as it was in early 2018, we believe that singular cryptojacking activity -- by individual website owners -- will decrease. However, we expect adversaries to find possibilities of deploying cryptojacking at an even larger scale to still be profitable. This stresses the importance of researching campaigns, as the reuse of techniques, tactics and procedures in deploying them provides an effective angle to detect and mitigate these malicious activities. With prices decreasing throughout 2018, one would expect that this problem will eventually solve itself. Apart from the discontinuation of Coinhive, there is no clear indication that this is the case, as Monero prices have started to recover in the first months of 2019. If this trend continues, we expect to experience another outbreak of large cryptojacking campaigns, as robust defenses are still not widely implemented."}37{"work_id": 2799796316, "title": "Privacy\u2010Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment", "abstract": "With the ever\u2010increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross\u2010platform data collaboration and service recommendation process. Locality\u2010Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy\u2010preserving distributed service recommendation. However, existing LSH\u2010based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH\u2010based service recommendation approach named SerRecmulti-qos, to protect users\u2019 privacy over multiple quality dimensions during the distributed mobile service recommendation process."}38{"work_id": 2965761488, "title": "The rise of blockchain technology in agriculture and food supply chains", "abstract": "Blockchain is an emerging digital technology allowing ubiquitous financial transactions among distributed untrusted parties, without the need of intermediaries such as banks. This article examines the impact of blockchain technology in agriculture and food supply chain, presents existing ongoing projects and initiatives, and discusses overall implications, challenges and potential, with a critical view over the maturity of these projects. Our findings indicate that blockchain is a promising technology towards a transparent supply chain of food, with many ongoing initiatives in various food products and food-related issues, but many barriers and challenges still exist, which hinder its wider popularity among farmers and systems. These challenges involve technical aspects, education, policies and regulatory frameworks."}39{"work_id": 4412162472, "title": "A scoping review of the governance of federated learning in healthcare", "abstract": "In healthcare, federated learning (FL) is emerging as a methodology to enable the analysis of large and disparate datasets while allowing custodians to retain sovereignty. While FL minimises data-sharing challenges, concerns surrounding ethics, privacy, maleficent use, and harm remain. These concerns can be managed by effective data governance. Data governance specifies procedural, relational, and structural mechanisms governing how data is captured, shared, and analysed, the resultant models and their use. However, limited insights exist on the optimal governance of this emerging technology. This study aims to develop a consolidated framework of the data governance mechanisms for FL in healthcare. A scoping review was performed, using deductive and inductive analysis of 39 articles. The framework includes twelve procedural, ten relational, and twelve structural mechanisms. The framework directs researchers to examine how to enact each mechanism and provides practitioners with insights into the mechanism to consider when governing FL."}40{"work_id": 4406636601, "title": "Challenges and opportunities for artificial intelligence in auditing: Evidence from the field", "abstract": "In this study we research the adoption of artificial intelligence (AI) in auditing by large public accounting firms, with emphasis on its challenges and opportunities. Some previous studies point to delayed adoption of AI in auditing due to regulations and the need for additional safeguards while others document extensive AI implementation. To address this dissensus, we conducted 22 interviews with experienced audit professionals. We find that \u201csimple AI\u201d technologies such as key data extraction from documents and optical character recognition are used widely in audits while \u201ccomplex AI\u201d tools are only being developed. We find RPA is used to automate repetitive administrative processes while the use of RPA for audit tasks is not as common. We also find that the main AI adoption challenges are related to transparency and explainability, AI bias, data privacy, robustness and reliability, fear of auditor overreliance on AI, and the need for AI guidance. We present ideas for addressing these challenges based on our research and lessons from other fields."}41{"work_id": 2914735843, "title": "Blockchain-Enabled Smart Contracts: Architecture, Applications, and Future Trends", "abstract": "In recent years, the rapid development of cryptocurrencies and their underlying blockchain technology has revived Szabo\u2019s original idea of smart contracts, i.e., computer protocols that are designed to automatically facilitate, verify, and enforce the negotiation and implementation of digital contracts without central authorities. Smart contracts can find a wide spectrum of potential application scenarios in the digital economy and intelligent industries, including financial services, management, healthcare, and Internet of Things, among others, and also have been integrated into the mainstream blockchain-based development platforms, such as Ethereum and Hyperledger. However, smart contracts are still far from mature, and major technical challenges such as security and privacy issues are still awaiting further research efforts. For instance, the most notorious case might be \u201cThe DAO Attack\u201d in June 2016, which led to more than $50 million Ether transferred into an adversary\u2019s account. In this paper, we strive to present a systematic and comprehensive overview of blockchain-enabled smart contracts, aiming at stimulating further research toward this emerging research area. We first introduced the operating mechanism and mainstream platforms of blockchain-enabled smart contracts, and proposed a research framework for smart contracts based on a novel six-layer architecture. Second, both the technical and legal challenges, as well as the recent research progresses, are listed. Third, we presented several typical application scenarios. Toward the end, we discussed the future development trends of smart contracts. This paper is aimed at providing helpful guidance and reference for future research efforts."}42{"work_id": 3123722958, "title": "Toward an ontology\u2010driven blockchain design for supply\u2010chain provenance", "abstract": "Summary An interesting research problem in our age of Big Data is that of determining provenance. Granular evaluation of provenance of physical goods (e.g., tracking ingredients of a pharmaceutical or demonstrating authenticity of luxury goods) has often not been possible with today's items that are produced and transported in complex, interorganizational, often internationally spanning supply chains. Recent adoptions of the Internet of Things and blockchain technologies give promise at better supply\u2010chain provenance. We are particularly interested in the blockchain, as many favored use cases of blockchain are for provenance tracking. We are also interested in applying ontologies, as there has been some work done on knowledge provenance, traceability, and food provenance using ontologies. In this paper, we make a case for why ontologies can contribute to blockchain design. To support this case, we analyze a traceability ontology and translate some of its representations to smart contracts that execute a provenance trace and enforce traceability constraints on the Ethereum blockchain platform."}43{"work_id": 2984937325, "title": "Trusted Execution Environments in Cloud Computing", "abstract": "Cloud technologies have become ubiquitous, being widely used by a variety of users: from individuals to large corporations. Without a doubt, clouds offer major benefits that make them attractive, such as elasticity, scalability, availability, and reliability. However, users' data privacy and security are two main concerns when data is processed in the cloud. Currently, users have to trust cloud providers for processing their data securely, not disclosing it to third parties maliciously or by mistake, and applying best security practices. Largely this trust is based on the cloud provider's reputation: it is in the interest of cloud providers to take security seriously; otherwise, they may lose customers. However this trust model does not provide the technical means of security assurance. \\n \\nTrusted Execution Environments (TEEs) can be used to change the situation. It is a very promising technology that enables secure computation on a remote host. Applying TEEs to cloud environments changes the trust model: customers no longer need to rely on the reputation of the cloud provider. The main reason is that a TEE provides the ability to execute arbitrary code in isolation from the underlying operating system or the hypervisor. Additionally, it is possible to remotely verify what code is running inside a TEE. The trust is based on the hardware and technically it is moved from the cloud provider to the hardware manufacturer. \\n \\nThis work addresses the following challenges when developing TEE applications and deploying TEEs in the cloud. First, how to build scalable applications while maintaining TEE security guarantees? Second, how to reconcile hardware-based TEEs with existing cloud practices? Third, how to utilize TEEs to provide trustworthy resource measurements in the cloud? \\n \\nThe contributions of this dissertation are developing two scalable Trusted Applications for cloud environments, a TEE migration framework to support Virtual Machine migration in the cloud, and a secure resource measurement framework based on TEEs. The contributions form an important step forward towards a wider deployment of TEEs in the cloud that opens opportunities for cloud providers and their clients to benefit from assurance based on hardware and stronger security guarantees."}44{"work_id": 3045674654, "title": "Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data", "abstract": "Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. Finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. Clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine."}45{"work_id": 3159080474, "title": "Federated Learning for Internet of Things: A Comprehensive Survey", "abstract": "Federated Learning for Internet of Things: A Comprehensive Survey"}46{"work_id": 2973816953, "title": "How Blockchain Will Change Organizations", "abstract": "For the last century, academics and business leaders have been shaping the practice of modern management. The main theories, tenets, and behaviors have enabled managers to build corporations, which have largely been hierarchical, insular, and vertically integrated. However, we believe that the technology underlying digital currencies such as bitcoin\u2014technology commonly known as blockchain\u2014will have profound effects on the nature of companies: how they are funded and managed, how they create value, and how they perform basic functions such as marketing, accounting, and incentivizing people. In some cases, software will eliminate the need for many management functions."}47{"work_id": 4293168393, "title": "New Directions in Cryptography", "abstract": "chapter Share on New Directions in Cryptography Authors: Whitfield Diffie View Profile , Martin E. Hellman View Profile Authors Info & Claims Democratizing Cryptography: The Work of Whitfield Diffie and Martin HellmanAugust 2022 Pages 365\u2013390https://doi.org/10.1145/3549993.3550007Published:26 August 2022Publication History 0citation45DownloadsMetricsTotal Citations0Total Downloads45Last 12 Months45Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access"}48{"work_id": 3163490558, "title": "Assessment of the Blockchain Technology Adoption for the Management of the Electronic Health Record Systems", "abstract": "Blockchain is an emerging technology that holds great promises in healthcare despite slow adoption and previous unsuccessful implementation projects. Blockchain adoption in healthcare has been slow, partly because healthcare is a heavily regulated and complex industry. Blockchain applications span various areas of healthcare such as patient data management, health information exchange, health supply chain management, financial and insurance claims, clinical trial, biomedical devices tracking, and pharmaceutical counterfeit. The main challenges with blockchain technology in healthcare are: scarcity of real applications, the high level of failing projects, and the need for various parties to function together. There is, however, a lack of research on how to assess the adoption and help healthcare organizations use blockchain for the management of the electronic healthcare records (EHR) systems in a comprehensive way incorporating multiple perspectives. The objective of this research is to develop a scoring model to evaluate healthcare organization's readiness to adopt blockchain for the management of the EHR systems. In this research, a literature review and expert feedback were used to identify the most important factors influencing blockchain adoption. The focus is on the application of the blockchain technology adoption for the management of the EHR systems. The Hierarchical Decision Modeling (HDM) methodology was used to elicit multiple expert's judgment to identify the relative importance of those factors influencing blockchain adoption. In addition, experts' feedback was used to identify the possible statuses an organization might have regarding each factor and the dynamic aspects of these factors was analyzed. Finally, two case studies of the blockchain adoption, Oregon Health and Science University (OHSU) hospital and a Medical City in Saudi Arabia, were conducted to demonstrate the practicality and value the research model brings to the research objective. The outcomes of the research present an identification of blockchain adoption impacting factors and their resultant rankings. The research identifies 17 factors as the most important factors influencing blockchain adoption and a healthcare organization's readiness for adoption. The factors are grouped into five perspective: financial, social, technical, organizational, and regulations & legal. The case studies are used to demonstrate how the model could be used to identify areas of deficiencies and propose corrective actions in the healthcare organization's capabilities. The goal is to prevent any possible issues, before the project starts, in order to increase chances of a successful blockchain adoption."}49{"work_id": 3036422000, "title": "A Survey of COVID-19 Contact Tracing Apps", "abstract": "The recent outbreak of COVID-19 has taken the world by surprise, forcing lockdowns and straining public health care systems. COVID-19 is known to be a highly infectious virus, and infected individuals do not initially exhibit symptoms, while some remain asymptomatic. Thus, a non-negligible fraction of the population can, at any given time, be a hidden source of transmissions. In response, many governments have shown great interest in smartphone contact tracing apps that help automate the difficult task of tracing all recent contacts of newly identified infected individuals. However, tracing apps have generated much discussion around their key attributes, including system architecture, data management, privacy, security, proximity estimation, and attack vulnerability. In this article, we provide the first comprehensive review of these much-discussed tracing app attributes. We also present an overview of many proposed tracing app examples, some of which have been deployed countrywide, and discuss the concerns users have reported regarding their usage. We close by outlining potential research directions for next-generation app design, which would facilitate improved tracing and security performance, as well as wide adoption by the population at large."}50{"work_id": 2460937040, "title": "Adversarial Examples in the Physical World", "abstract": "Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a human observer does not even notice the modification at all, yet the classifier still makes a mistake. Adversarial examples pose security concerns because they could be used to perform an attack on machine learning systems, even if the adversary has no access to the underlying model. Up to now, all previous work has assumed a threat model in which the adversary can feed data directly into the machine learning classifier. This is not always the case for systems operating in the physical world, for example those which are using signals from cameras and other sensors as input. This paper shows that even in such physical world scenarios, machine learning systems are vulnerable to adversarial examples. We demonstrate this by feeding adversarial images obtained from a cell-phone camera to an ImageNet Inception classifier and measuring the classification accuracy of the system. We find that a large fraction of adversarial examples are classified incorrectly even when perceived through the camera."}51{"work_id": 3201596224, "title": "ELLIPTIC CURVE DIGITAL SIGNATURE ALGORITHM", "abstract": "Elliptic curve cryptography has been a remarkable development in the history of cryptography thanks to the properties provided by the implementation of elliptic curve cryptography. Elliptic curve cryptography has proven to be adaptable given its broad presence across various electronics of diering sizes and capabilities. Elliptic curve cryptography is known for utilizing the discrete logarithm problem, modern algebra, and elliptic curves that have encouraged continued research into elliptic curve cryptography in its advantages and restrictions. The current comprehension of elliptic curves as well as continuing advancements and employments of elliptic curve cryptography have yielded the elliptic curve digital signature algorithm (ECDSA) which has become an alternative to the primary Digital Signature Algorithm. TheECDSA provides advantages of elliptic curve cryptography to the function of the digital signature algorithm to authenticate and protect transmissions between involved parties. Such can be explored in the securing of bitcoinrelated transactions where the completely electronic, decentralized currencywould face a multitude of cyber threats and require safeguards to remaintrustworthy."}52{"work_id": 2913851998, "title": "Status report on the first round of the NIST post-quantum cryptography standardization process", "abstract": "The Information Technology Laboratory (ITL) at the National Institute of Standards and Technology (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the Nation's measurement and standards infrastructure.ITL develops tests, test methods, reference data, proof of concept implementations, and technical analyses to advance the development and productive use of information technology.ITL's responsibilities include the development of management, administrative, technical, and physical standards and guidelines for the cost-effective security and privacy of other than national security-related information in federal information systems."}53{"work_id": 3021970994, "title": "Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study", "abstract": "BACKGROUND: The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs. One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19. Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention. OBJECTIVE: The objective of this study is to investigate the user acceptability of a contact-tracing app in five countries hit by the pandemic. METHODS: We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections. We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States. We measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries. RESULTS: We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates. We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption. CONCLUSIONS: Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19."}54{"work_id": 3018102029, "title": "SplitFed: When Federated Learning Meets Split Learning", "abstract": "Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Both follow a model-to-data scenario; clients train and test machine learning models without sharing raw data. SL provides better model privacy than FL due to the machine learning model architecture split between clients and the server. Moreover, the split model makes SL a better option for resource-constrained environments. However, SL performs slower than FL due to the relay-based training across multiple clients. In this regard, this paper presents a novel approach, named splitfed learning (SFL), that amalgamates the two approaches eliminating their inherent drawbacks, along with a refined architectural configuration incorporating differential privacy and PixelDP to enhance data privacy and model robustness. Our analysis and empirical results demonstrate that (pure) SFL provides similar test accuracy and communication efficiency as SL while significantly decreasing its computation time per global epoch than in SL for multiple clients. Furthermore, as in SL, its communication efficiency over FL improves with the number of clients. Besides, the performance of SFL with privacy and robustness measures is further evaluated under extended experimental settings."}55{"work_id": 7127055292, "title": "Artificial intelligence in higher education: a systematic review of its impact on student engagement and the mediating role of teaching methods", "abstract": "Introduction Artificial Intelligence (AI) is increasingly integrated into higher education to personalize instruction and support student engagement. However, the mediating role of teaching methods in this process remains underexplored. Methods This systematic review analyzed 73 peer-reviewed articles published between 2015 and early 2025, retrieved from Scopus and Web of Science, following PRISMA guidelines. Studies were screened based on predefined inclusion criteria and coded using a structured framework that examined AI types, engagement outcomes, and instructional strategies. Results Findings reveal that AI tools\u2014such as chatbots, adaptive systems, and predictive analytics\u2014enhance engagement most effectively when embedded within interactive pedagogies like flipped classrooms, project-based learning, and scaffolded feedback loops. To conceptualize this relationship, we introduce the PMAISE model (Pedagogical Mediation of AI for Student Engagement), which maps the alignment between AI technologies, pedagogical strategies, and the affective, behavioral, and cognitive dimensions of engagement. Concrete examples from recent studies demonstrate how teaching methods amplify or inhibit the effects of AI tools. The review also critically examines emerging concerns related to ethics, data privacy, and structural barriers to equitable AI adoption. Discussion This study offers a conceptual and practical framework for integrating AI into higher education in a context-sensitive, evidence-based, and pedagogically meaningful manner, highlighting the crucial role of thoughtful pedagogical mediation in maximizing AI\u2019s educational benefits."}56{"work_id": 4382463479, "title": "FedALA: Adaptive Local Aggregation for Personalized Federated Learning", "abstract": "A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. The key component of FedALA is an Adaptive Local Aggregation (ALA) module, which can adaptively aggregate the downloaded global model and local model towards the local objective on each client to initialize the local model before training in each iteration. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains. FedALA outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy. Furthermore, we also apply ALA module to other federated learning methods and achieve up to 24.19% improvement in test accuracy. Code is available at https://github.com/TsingZ0/FedALA."}57{"work_id": 652269645, "title": "A Design and Analysis of Graphical Password", "abstract": "The most common computer authentication method is to use alphanumerical usernames and passwords. This method has been shown to have significant drawbacks. For example, users tend to pick passwords that can be easily guessed. On the other hand, if a password is hard to guess, then it is often hard to remember. To address this problem, some researchers have developed authentication methods that use pictures as passwords. In this paper, I conduct a comprehensive survey of the existing graphical password techniques. I classify these techniques into two categories: recognition-based and recall-based approaches. I discuss the strengths and limitations of each method and point out the future research directions in this area. I also developed three new techniques against the common problem exists in the present graphical password techniques. In this thesis, the scheme of each new technique will be proposed; the advantages of each technique will be discussed; and the future work will be anticipated."}58{"work_id": 4400662300, "title": "Collaborative Networks: Integrating Blockchain for Enhanced Trust and Transparency", "abstract": "Blockchain technology has become a disruptive force in many different industries because it provides unparalleled security, transparency, and efficiency. In the context of cooperative networks, Blockchain's decentralized architecture provides a solid basis for enhancing trust, streamlining processes, and fostering innovation. This chapter examines how Blockchain can be used with cooperative networks and how it could fundamentally alter the way entities interact and communicate. Important areas of focus include Blockchain's technological foundations, applications in collaborative environments, and case studies that highlight its impacts. By examining the challenges and opportunities presented by this convergence, we intend to provide a comprehensive understanding of how Blockchain can revolutionize cooperative ecosystems. The field of scholarly communication is undergoing a significant transition due to technological advancements. This chapter looks at how blockchain technology and collaborative networks could alter contemporary living. We examine the basic shortcomings of the existing centralized model and discuss how the core concepts of immutability, transparency, and decentralization found in Blockchain technology can assist in resolving these problems. This paper explores the relationship between collaborative networks and blockchain technology. We start by outlining the fundamental ideas behind Blockchain technology and how it has developed. The use of Blockchain is then examined in a variety of cooperative settings, with its advantages and possible drawbacks discussed. We demonstrate the real-world effects of Blockchain-enabled collaborative networks across various industries with a number of case studies."}59{"work_id": 7154488736, "title": "Predicting Mental Health Challenges Among University Students Using a Federated CNN-BiLSTM-Attention Framework", "abstract": "Early detection and effective management of rising mental health issues among university students is a major challenge. This work proposes Fed-CBA, a novel deep learning framework combining CNN, BiLSTM, and Attention to predict future mental health risks. Fed-CBA integrates multiple data sources (LMS, EMA surveys, wearables, lifestyle data) and operates in a decentralized, privacy-preserving manner using FedAvg and differential privacy. It also provides explainable predictions through SHAP and LIME. Experimental results show that Fed-CBA outperforms DNN, CNN, and LSTM models, while also improving fairness (EOR) and interpretability. Overall, the framework enables early detection and supports personalized, equitable student mental health services."}60{"work_id": 4380356557, "title": "Blockchain Technology in Renovating Healthcare", "abstract": "The positive effect of technology on health care is known as digital transformation. Wearable fitness technology, telemedicine, and AI-enabled medical devices are just a few examples of how health care is undergoing a digital transformation. These are intended to revolutionize the health-care industry by improving patient care, streamlining operations, and lowering costs. They are still facing significant challenges in patient data security and privacy, invoicing and payment processing, medical supply chain, and drug integrity. Blockchain technology can absolve the healthcare industry from facing these challenges; it can establish a blockchain of medical records. Blockchain is considered highly secure, transparent, and immune to hackers due to its digital encryption. This article examined the potential of blockchain technology to improve the security, privacy, and interoperability of healthcare data, and it suggested a few advanced uses of blockchain in the health care domain after a thorough analysis of the existing major challenges in the sector."}61{"work_id": 4322207773, "title": "Automating Behavior-based Ransomware Analysis, Detection, and Classification Using Machine Learning", "abstract": "Ransomware is malware that hijacks a victim's data using encryption and demands a ransom in exchange for the decryption key. Ransomware has gained prominence due to its attack vector and the irreversible nature of damage to data. Ransomware has indiscriminately attacked individuals and organizations worldwide, disrupting their businesses and services. The number of successful ransomware attacks across the globe highlights the inadequacy of existing ransomware defense. Static and dynamic analysis are two popular approaches to malware analysis. The former does not require execution of the malware binary, whereas the latter requires executing the binary in a controlled environment. Static analysis-based detection, e.g., signature-matching, is widely adopted by commercial antivirus solutions but can be thwarted by evasion techniques, e.g., polymorphism and code obfuscation, utilized in modern malware. Consequently, dynamic analysis-based or behavior-based detection approaches have gained popularity because malware behavior cannot be changed entirely across its variants. Both signature and behavior-based detection complement each other. Behavior-based ransomware detection comes with certain challenges and problems, such as data high-dimensionality that occurs because a process may execute thousands of API calls per second. Manual inspection of these API calls for feature engineering requires an expert and is a time-intensive task. Another problem with some existing ransomware detection models is the reliance on handcrafted malice scoring functions that assign scores to the processes describing their threat levels. Other challenges to ransomware detection research include the limited availability of ransomware data sets that can be used with Machine Learning (ML) methods and their reuse scope. The scope of reuse of available data sets is limited because of their format, e.g., sequential data may be used with recurrent neural networks but not with commonly used ML-based classification algorithms, and focus, e.g., network activity and filesystem activity. For the above-mentioned reasons, ransomware detection research is generally followed by ransomware analysis. However, to the best of our knowledge, not many of the existing ransomware behavior analysis studies discuss the challenges involved in the process. This thesis aims to automate the solutions to the problems related to ransomware behavior detection and classification using evolutionary computation methods, i.e., particle swarm optimization and genetic programming, and deep neural networks, i.e., long short-term memory. This thesis proposes a wrapper feature selection method to address the high dimensionality in ransomware behavior data. The proposed method utilizes particle swarm optimization to automatically select a suitable number of features from each feature group and therefore does not require expert input. This thesis further proposes an automated method of evolving malice scoring models for ransomware detection. The proposed method formulates the problem as a symbolic regression problem and solves it using genetic programming. Unlike existing methods, the proposed method does not require expert knowledge to design the model. Furthermore, this thesis proposes an automated behavior analysis framework for highlighting challenges associated with ransomware behavior analysis and solutions to these challenges. Finally, this thesis proposes a new representation of the API call sequences that combines the API call names and important call arguments. The proposed representation of the API call sequences helped improve ransomware early detection performance. All the methods proposed in this thesis either automate the existing manual solutions or achieve comparable or better performance compared to existing methods."}62{"work_id": 3014084098, "title": "Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs", "abstract": "Contact tracing is an essential tool for public health officials and local communities to fight the spread of novel diseases, such as for the COVID-19 pandemic. The Singaporean government just released a mobile phone app, TraceTogether, that is designed to assist health officials in tracking down exposures after an infected individual is identified. However, there are important privacy implications of the existence of such tracking apps. Here, we analyze some of those implications and discuss ways of ameliorating the privacy concerns without decreasing usefulness to public health. We hope in writing this document to ensure that privacy is a central feature of conversations surrounding mobile contact tracing apps and to encourage community efforts to develop alternative effective solutions with stronger privacy protection for the users. Importantly, though we discuss potential modifications, this document is not meant as a formal research paper, but instead is a response to some of the privacy characteristics of direct contact tracing apps like TraceTogether and an early-stage Request for Comments to the community. Date written: 2020-03-24 Minor correction: 2020-03-30"}63{"work_id": 4412565294, "title": "A Survey of Machine Unlearning", "abstract": "Today, computer systems hold large amounts of personal data. Yet while such an abundance of data allows breakthroughs in AI, and especially machine learning, its existence can be a threat to user privacy, and it can weaken the bonds of trust between humans and AI. Recent regulations now require that, on request, private information about a user must be removed both from computer systems and from machine learning models\u2014this legislation is more colloquially called \u201cthe right to be forgotten.\u201d While removing data from back-end databases should be straightforward, it is not sufficient in the AI context as machine learning models often \u201cremember\u201d the old data. Contemporary adversarial attacks on trained models have proven that we can learn whether an instance or an attribute belonged to the training data. This phenomenon calls for a new paradigm, namely machine unlearning , to make machine learning models forget about particular data. It turns out that recent works on machine unlearning have not been able to completely solve the problem due to the lack of common frameworks and resources. Therefore, this article aspires to present a comprehensive examination of machine unlearning\u2019s concepts, designs, methods, and applications. Specifically, as a category collection of cutting-edge studies, the intention behind this article is to serve as a comprehensive resource for researchers and practitioners seeking an introduction to machine unlearning and its formulations, design criteria, removal requests, algorithms, and applications. In addition, we aim to highlight the key findings, current trends, and new research areas that have not yet featured the use of machine unlearning but could benefit greatly from it. We hope that this survey serves as a valuable resource for machine learning researchers and those seeking to innovate privacy technologies. Our resources are publicly available at https://github.com/tamlhp/awesome-machine-unlearning ."}64{"work_id": 4320717855, "title": "Blockchain for the metaverse: A Review", "abstract": "Since Facebook officially changed its name to Meta in Oct. 2021, the metaverse has become a new norm of social networks and three-dimensional (3D) virtual worlds. The metaverse aims to bring 3D immersive and personalized experiences to users by leveraging many pertinent technologies. Despite great attention and benefits, a natural question in the metaverse is how to secure its users' digital content and data. In this regard, blockchain is a promising solution owing to its distinct features of decentralization, immutability, and transparency. To better understand the role of blockchain in the metaverse, we aim to provide an extensive survey on the applications of blockchain for the metaverse. We first present a preliminary to blockchain and the metaverse and highlight the motivations behind the use of blockchain for the metaverse. Next, we extensively discuss blockchain-based methods for the metaverse from technical perspectives, such as data acquisition, data storage, data sharing, data interoperability, and data privacy preservation. For each perspective, we first discuss the technical challenges of the metaverse and then highlight how blockchain can help. Moreover, we investigate the impact of blockchain on key-enabling technologies in the metaverse, including Internet-of-Things, digital twins, multi-sensory and immersive applications, artificial intelligence, and big data. We also present some major projects to showcase the role of blockchain in metaverse applications and services. Finally, we present some promising directions to drive further research innovations and developments toward the use of blockchain in the metaverse in the future."}65{"work_id": 7154469464, "title": "Cryptanalysis and Improvement of a Video Cryptosystem via Chaos and S-Box", "abstract": "In recent years, chaos-based multimedia cryptosystems have gained prominence due to their nonlinear properties, such as sensitivity to initial conditions and long-term unpredictability, which parallel the cryptographic requirements of confusion and diffusion. However, many such systems lack standardization and deviate from secure design principles, resulting in practical vulnerabilities. This article presents a comprehensive cryptanalysis of a widely cited video cryptosystem. The target system combines S-box substitution\u2014derived from either a 12D chaotic map or the Ikeda delay differential equation (DDE)\u2014with a Cipher Block Chaining (CBC) diffusion scheme. Through an analysis of the encryption structure, fundamental design defects are identified. Specifically, the CBC diffusion mechanism lacks key dependence, and the system exhibits an excessive reliance on fixed, publicly exposed S-boxes. These vulnerabilities are demonstrated through chosen-plaintext attacks, known-plaintext attacks, and chosen-ciphertext attacks. Experimental results confirm that the S-box can be fully reconstructed, thereby facilitating the complete decryption of video content in the absence of the secret key. Beyond exposing vulnerabilities, this study offers constructive contributions by proposing six concrete improvement strategies, including dynamic key generation, structural enhancement, and dynamic S-box indexing. This work provides practical suggestions for designing secure chaos-based multimedia cryptosystems, offering valuable references for future research and promoting the development of efficient encryption systems."}66{"work_id": 4389365236, "title": "Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review", "abstract": "The rapid evolution of e-learning platforms, propelled by advancements in artificial intelligence (AI) and machine learning (ML), presents a transformative potential in education. This dynamic landscape necessitates an exploration of AI/ML integration in adaptive learning systems to enhance educational outcomes. This study aims to map the current utilization of AI/ML in e-learning for adaptive learning, elucidating the benefits and challenges of such integration and assessing its impact on student engagement, retention, and performance. A comprehensive literature review was conducted, focusing on articles published from 2010 onwards, to document the integration of AI/ML in e-learning. The review analyzed 63 articles, employing a systematic approach to evaluate the deployment of adaptive learning algorithms and their educational implications. Findings reveal that AI/ML algorithms are instrumental in personalizing learning experiences. These technologies have been shown to optimize learning paths, enhance engagement, and improve academic performance, with some studies reporting increased test scores. The integration of AI/ML in e-learning platforms significantly contributes to the personalization and effectiveness of the educational process. Despite challenges like data privacy and the complexity of AI/ML systems, the results underscore the potential of adaptive learning to revolutionize education by catering to individual learner needs."}67{"work_id": 4402275599, "title": "Implementing Blockchain Technology for Optimized Supply Chain and Enhanced Sustainability", "abstract": "Supply chain efficiency, transparency, and sustainability can be enhanced using blockchain technology. Blockchain enables a company to accurately track raw material origin to finished products, which ensures standard quality and sustainability. Furthermore, blockchain improves partnerships between supply chain stakeholders by providing a confident, common platform for sharing data. The influence of implementing blockchain technology on supply chain sustainability includes reduced wastage, increased resource transparency, monitoring of social standards, and reduced operational costs. The challenges of blockchain implementation include scalability, incorporation with present systems, lack of expertise, safety and confidentiality, and regulatory uncertainty. Companies can tackle these challenges through collaborative approaches and technical improvements. In general, blockchain technology significantly enhances supply chain sustainability and efficiency, which provides opportunities for creative business solutions."}68{"work_id": 7133255133, "title": "Federated Learning: A Survey of Core Challenges, Current Methods, and Opportunities", "abstract": "Federated Learning (FL) has emerged as a transformative distributed learning paradigm that enables collaborative model training without sharing raw data, thereby preserving privacy across large, diverse, and geographically dispersed clients. Despite its rapid adoption in mobile networks, IoT systems, healthcare, finance, and edge intelligence, FL continues to face several persistent and interdependent challenges that hinder its scalability, efficiency, and real-world deployment. In this survey, we present a systematic examination of six core challenges in federated learning: heterogeneity, computation overhead, communication bottlenecks, client selection, aggregation and optimization, and privacy preservation. We analyze how these challenges manifest across the full FL pipeline, from local training and client participation to global model aggregation and distribution, and examine their impact on model performance, convergence behavior, fairness, and system reliability. Furthermore, we synthesize representative state-of-the-art approaches proposed to address each challenge and discuss their underlying assumptions, trade-offs, and limitations in practical deployments. Finally, we identify open research problems and outline promising directions for developing more robust, scalable, and efficient federated learning systems. This survey aims to serve as a comprehensive reference for researchers and practitioners seeking a unified understanding of the fundamental challenges shaping modern federated learning."}69{"work_id": 2784387076, "title": "Blockchains and the economic institutions of capitalism", "abstract": "Abstract Blockchains are a new digital technology that combines peer-to-peer network computing and cryptography to create an immutable decentralised public ledger. Where the ledger records money, a blockchain is a cryptocurrency, such as Bitcoin; but ledger entries can record any data structure, including property titles, identity and certification, contracts, and so on. We argue that the economics of blockchains extend beyond analysis of a new general purpose technology and its disruptive Schumpeterian consequences to the broader idea that blockchains are an institutional technology. We consider several examples of blockchain-based economic coordination and governance. We claim that blockchains are an instance of institutional evolution."}70{"work_id": 4393156671, "title": "FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning", "abstract": "Recently, Heterogeneous Federated Learning (HtFL) has attracted attention due to its ability to support heterogeneous models and data. To reduce the high communication cost of transmitting model parameters, a major challenge in HtFL, prototype-based HtFL methods are proposed to solely share class representatives, a.k.a, prototypes, among heterogeneous clients while maintaining the privacy of clients\u2019 models. However, these prototypes are naively aggregated into global prototypes on the server using weighted averaging, resulting in suboptimal global knowledge which negatively impacts the performance of clients. To overcome this challenge, we introduce a novel HtFL approach called FedTGP, which leverages our Adaptive-margin-enhanced Contrastive Learning (ACL) to learn Trainable Global Prototypes (TGP) on the server. By incorporating ACL, our approach enhances prototype separability while preserving semantic meaning. Extensive experiments with twelve heterogeneous models demonstrate that our FedTGP surpasses state-of-the-art methods by up to 9.08% in accuracy while maintaining the communication and privacy advantages of prototype-based HtFL. Our code is available at https://github.com/TsingZ0/FedTGP."}71{"work_id": 2783535300, "title": "The Supply Chain Has No Clothes: Technology Adoption of Blockchain for Supply Chain Transparency", "abstract": "Blockchain technology, popularized by Bitcoin cryptocurrency, is characterized as an open-source, decentralized, distributed database for storing transaction information. Rather than relying on centralized intermediaries (e.g., banks) this technology allows two parties to transact directly using duplicate, linked ledgers called blockchains. This makes transactions considerably more transparent than those provided by centralized systems. As a result, transactions are executed without relying on explicit trust [of a third party], but on the distributed trust based on the consensus of the network (i.e., other blockchain users). Applying this technology to improve supply chain transparency has many possibilities. Every product has a long and storied history. However, much of this history is presently obscured. Often, when negative practices are exposed, they quickly escalate to scandalous, and financially crippling proportions. There are many recent examples, such as the exposure of child labor upstream in the manufacturing process and the unethical use of rainforest resources. Blockchain may bring supply chain transparency to a new level, but presently academic and managerial adoption of blockchain technologies is limited by our understanding. To address this issue, this research uses the Unified Theory of Acceptance and Use of Technology (UTAUT) and the concept of technology innovation adoption as a foundational framework for supply chain traceability. A conceptual model is developed and the research culminates with supply chain implications of blockchain that are inspired by theory and literature review."}72{"work_id": 4220942018, "title": "Public and private blockchains", "abstract": "In the previous section we discussed cryptocurrencies, but not all blockchains are used for this, many are used for more restricted access purposes, like supply chain management. We look at these private blockchains and what the differences are, discussing Certificates and Certificate Authorities, Membership Service Providers, Channels and the World State."}73{"work_id": 4289548059, "title": "Detecting Cross-Function Reentrancy from EVM Traces", "abstract": "Reentrancy remains one of the most critical vulnerabilities affecting Ethereum smart contracts. While many existing analysis tools focus on detecting classical single-function reentrancy, more complex forms such as cross-function reentrancy are harder to identify because they depend on execution semantics and interactions between multiple functions. In this work, we study reentrancy at the level of Ethereum Virtual Machine (EVM) execution traces. We extend the TxSpector framework with new Datalog-based detection rules designed to capture cross-function reentrancy patterns. To support this analysis, we also modernize the trace extraction component by adapting it to recent versions of the Ethereum client and updated EVM instructions. The proposed approach is evaluated on real Ethereum on-chain transaction traces. The results show that our method is able to detect cross-function reentrancy behaviors that are not captured by the original TxSpector rules, demonstrating the effectiveness of pattern-based logic detection at the EVM execution level."}74{"work_id": 4226075590, "title": "Status report on the third round of the NIST Post-Quantum Cryptography Standardization process", "abstract": "The National Institute of Standards and Technology is in the process of selecting public-key cryptographic algorithms through a public, competition-like process. The new public-key cryptography standards will specify additional digital signature, public-key encryption, and key-establishment algorithms to augment Federal Information Processing Standard (FIPS) 186-4, Digital Signature Standard (DSS), as well as NIST Special Publication (SP) 800-56A Revision 3, Recommendation for Pair-Wise Key-Establishment Schemes Using Discrete Logarithm Cryptography, and SP 800-56B Revision 2, Recommendation for Pair-Wise Key Establishment Using Integer Factorization Cryptography. It is intended that these algorithms will be capable of protecting sensitive information well into the foreseeable future, including after the advent of quantum computers. The first round of the NIST Post-Quantum Cryptography Standardization Process began in December 2017 with 69 candidate algorithms that met both the minimum acceptance criteria and submission requirements. The first round lasted until January 2019, during which candidate algorithms were evaluated based on their security, performance, and other characteristics. NIST selected 26 algorithms to advance to the second round for more analysis. The second round continued until July 2020, after which seven 'finalist' and eight 'alternate' candidate algorithms were selected to move into the third round. This report describes the evaluation and selection process, based on public feedback and internal review, of the third-round candidates. The report summarizes each of the 15 third-round candidate algorithms and identifies those selected for standardization, as well as those that will continue to be evaluated in a fourth round of analysis. The public-key encryption and key-establishment algorithm that will be standardized is CRYSTALS-Kyber. The digital signatures that will be standardized are CRYSTALS-Dilithium, Falcon, and SPHINCS+. While there are multiple signature algorithms selected, NIST recommends CRYSTALS-Dilithium as the primary algorithm to be implemented. In addition, four of the alternate key-establishment candidate algorithms will advance to a fourth round of evaluation: BIKE, Classic McEliece, HQC, and SIKE. These candidates are still being considered for future standardization. NIST will also issue a new Call for Proposals for public-key digital signature algorithms to augment and diversify its signature portfolio."}75{"work_id": 7137155252, "title": "Differentially Private Federated Learning: A Systematic Review", "abstract": "In recent years, privacy and security concerns in machine learning have promoted trusted federated learning to the forefront of research. Differential privacy has emerged as the de facto standard for privacy protection in federated learning due to its rigorous mathematical foundation and provable guarantee. Despite extensive research on algorithms that incorporate differential privacy within federated learning, there remains an evident deficiency in systematic reviews that categorize and synthesize these studies. Our work presents a systematic overview of the differentially private federated learning. Existing taxonomies have not adequately considered objects and level of privacy protection provided by various differential privacy models in federated learning. To rectify this gap, we propose a new taxonomy of differentially private federated learning based on definition and guarantee of various differential privacy models and federated scenarios. Our classification allows for a clear delineation of the protected objects across various differential privacy models and their respective neighborhood levels within federated learning environments. Furthermore, we explore the applications of differential privacy in federated learning scenarios. Our work provide valuable insights into privacy-preserving federated learning and suggest practical directions for future research."}76{"work_id": 2907683311, "title": "Blockchain for AI: Review and Open Research Challenges", "abstract": "Recently, artificial intelligence (AI) and blockchain have become two of the most trending and disruptive technologies. Blockchain technology has the ability to automate payment in cryptocurrency and to provide access to a shared ledger of data, transactions, and logs in a decentralized, secure, and trusted manner. Also with smart contracts, blockchain has the ability to govern interactions among participants with no intermediary or a trusted third party. AI, on the other hand, offers intelligence and decision-making capabilities for machines similar to humans. In this paper, we present a detailed survey on blockchain applications for AI. We review the literature, tabulate, and summarize the emerging blockchain applications, platforms, and protocols specifically targeting AI area. We also identify and discuss open research challenges of utilizing blockchain technologies for AI."}77{"work_id": 4312634912, "title": "Layer-wised Model Aggregation for Personalized Federated Learning", "abstract": "Personalized Federated Learning (pFL) not only can capture the common priors from broad range of distributed data, but also support customized models for heterogeneous clients. Researches over the past few years have applied the weighted aggregation manner to produce personalized models, where the weights are determined by calibrating the distance of the entire model parameters or loss values, and have yet to consider the layer-level impacts to the aggregation process, leading to lagged model convergence and inadequate personalization over non-IID datasets. In this paper, we propose a novel pFL training framework dubbed Layer-wised Personalized Federated learning (pFedLA) that can discern the importance of each layer from different clients, and thus is able to optimize the personalized model aggregation for clients with heterogeneous data. Specifically, we employ a dedicated hyper-network per client on the server side, which is trained to identify the mutual contribution factors at layer granularity. Meanwhile, a parameterized mechanism is introduced to update the layer-wised aggregation weights to progressively exploit the inter-user similarity and realize accurate model personalization. Extensive experiments are conducted over different models and learning tasks, and we show that the proposed methods achieve significantly higher performance than state-of-the-art pFL methods."}78{"work_id": 4402716118, "title": "FedAS: Bridging Inconsistency in Personalized Federated Learning", "abstract": "Personalized Federated Learning (PFL) is primarily de-signed to provide customized models for each client to better fit the non-iid distributed client data, which is a inherent challenge in Federated Learning. However, current PFL methods suffer from inconsistencies in both intra-client and inter-client levels: 1) The intra-client inconsistency stems from the asynchronous update strategy for personalized and shared parameters. In PFL, clients update their shared parameters to communicate and learn from others, while keeping personalized parts unchanged, leading to poor coordination between these two components. 2) The Inter-client inconsistency arises from \u201cstragglers\u201d - inactive clients that communicate and train with the server less frequently. This results in their under-trained personalized models and impedes the collaborative training stage for other clients. In this paper, we present a novel PFL framework named FedAS, which uses Federated Parameter-Alignment and Client-Synchronization to overcome above challenges. Initially, we enhance the localization of global parameters by infusing them with local insights. We make the shared parts learn from previous model, thereby increasing their local relevance and reducing the impact of parameter inconsistency. Furthermore, we design a robust aggregation method to mitigate the impact of stragglers by preventing the incorporation of their under-trained knowledge into aggregated model. Experimental results on CifarlO and CifarlOO validate the effectiveness of our FedAS in achieving better performance and robustness against data heterogeneity."}79{"work_id": 6906013109, "title": "Blockchain Adoption in Organizations : Technical Barriers and Potential Solutions", "abstract": "Blockchain technology is considered a potential solution to many challenges businesses and public institutions face within the digital transformation. Specifically, blockchain-based IT solutions can enable cross-organizational digital interactions for which no universally trusted intermediary exists for economic, political, or social reasons. Related work suggests that the technical properties of blockchain networks align very well with the requirements of many applications and emphasizes management-related adoption challenges, for instance, identifying business models or developing effective governance approaches for decentralized systems. However, when taking a closer look at blockchain projects in organizations in practice, several technical issues appear significantly more relevant than anticipated. This doctoral thesis focuses on three essential representatives of these technical adoption barriers: (1) high electricity demand, (2) low performance accompanied by high operating costs, and (3) excessive transparency. I discuss to which extent these issues are problematic when implementing blockchain technology in organizations and survey solutions already being explored in academia and practice. Furthermore, I design and evaluate novel approaches for blockchain-based IT systems. I find that sustainability issues are not problematic in general but rather an undue extrapolation from Bitcoin's high electricity needs. In contrast, performance and data visibility aspects are more fundamental and difficult to solve for organizations. According to my analysis, permissioned blockchains that have been developed to address these issues often do not provide a sufficient solution. Serverless blockchains may be useful in specific scenarios in which high performance, favorable cost stuctures, seamless integration with cloud applications, as well as fine-granular access control are crucial aspects while a high degree of centralization is acceptable. In general, blockchain-based solutions in organizations that address performance and data visibility requirements will involve complex combinations of different cryptographic components and communication layers, with verifiable computation techniques such as zk-SNARKs and the bilateral exchange of verifiable attestations according to the self-sovereign identity paradigm playing a key role."}80{"work_id": 1598516026, "title": "Scantegrity II Municipal Election at Takoma Park: The First E2E Binding Governmental Election with Ballot Privacy", "abstract": "On November 3, 2009, voters in Takoma Park, Maryland, cast ballots for the mayor and city council members using the Scantegrity II voting system-the first time any end-to-end (E2E) voting system with ballot privacy has been used in a binding governmental election.This case study describes the various efforts that went into the election-including the improved design and implementation of the voting system, streamlined procedures, agreements with the city, and assessments of the experiences of voters and poll workers.The election, with 1728 voters from six wards, involved paper ballots with invisible-ink confirmation codes, instant-runoff voting with write-ins, early and absentee (mail-in) voting, dual-language ballots, provisional ballots, privacy sleeves, any-which-way scanning with parallel conventional desktop scanners, end-to-end verifiability based on optional web-based voter verification of votes cast, a full hand recount, thresholded authorities, three independent outside auditors, fully-disclosed software, and exit surveys for voters and pollworkers.Despite some glitches, the use of Scantegrity II was a success, demonstrating that E2E cryptographic voting systems can be effectively used and accepted by the general public."}81{"work_id": 2909202584, "title": "Privacy-Preserving Energy Trading Using Consortium Blockchain in Smart Grid", "abstract": "Implementing blockchain techniques has enabled secure smart trading in many realms, e.g. neighboring energy trading. However, trading information recorded on the blockchain also brings privacy concerns. Attackers can utilize data mining algorithms to obtain users' privacy, specially, when the user group is located in nearby geographic positions. In this paper, we present a consortium blockchain-oriented approach to solve the problem of privacy leakage without restricting trading functions. The proposed approach mainly addresses energy trading users' privacy in smart grid and screens the distribution of energy sale of sellers deriving from the fact that various energy trading volumes can be mined to detect its relationships with other information, such as physical location and energy usage. Experiment evaluations have demonstrated the effectiveness of the proposed approach."}82{"work_id": 4413785573, "title": "Encrypt a Story: A Video Segment Encryption Method Based on the Discrete Sinusoidal Memristive Rulkov Neuron", "abstract": "Traditional video encryption methods protect video content by encrypting each frame individually. However, in resource-constrained environments, this approach consumes significant computational resources. To overcome this challenge, this paper proposes a novel method called \u201cEncrypt a story (EAS)\u201d, which aims to enhance encryption efficiency by focusing on encrypting specific segments of the video rather than encrypting each frame. The EAS refers to selecting segments in the time dimension of the video that contain important information or key events for encryption. This method leverages video segmentation techniques to focus encryption efforts on continuous key frames, significantly reducing the consumption of computational resources. To address the need for a large number of key streams during the encryption process, this paper proposes a discrete sinusoidal memristive Rulkov neuron map (DSM-RNM). Through attractor analysis, complexity comparison, Lyapunov exponent, and NIST tests, we validated its ability to generate high-performance pseudorandom sequences, which significantly enhances the security of the encryption algorithm. Notably, the DSM-RNM is shown to exhibit a phenomenon of infinitely coexisting attractors. Furthermore, by constructing a digital circuit to capture the attractors of the DSM-RNM, its potential for industrial applications is demonstrated. Evaluation results show that the EAS saves approximately 90% of the time while ensuring security, exhibiting strong practicality and efficiency"}83{"work_id": 2995735216, "title": "Fair Resource Allocation in Federated Learning", "abstract": "Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the devices. In this work, we propose q-Fair Federated Learning (q-FFL), a novel optimization objective inspired by fair resource allocation in wireless networks that encourages a more fair (i.e., more uniform) accuracy distribution across devices in federated networks. To solve q-FFL, we devise a communication-efficient method, q-FedAvg, that is suited to federated networks. We validate both the effectiveness of q-FFL and the efficiency of q-FedAvg on a suite of federated datasets with both convex and non-convex models, and show that q-FFL (along with q-FedAvg) outperforms existing baselines in terms of the resulting fairness, flexibility, and efficiency."}84{"work_id": 2805930283, "title": "A Review on the Use of Blockchain for the Internet of Things", "abstract": "The paradigm of Internet of Things (IoT) is paving the way for a world, where many of our daily objects will be interconnected and will interact with their environment in order to collect information and automate certain tasks. Such a vision requires, among other things, seamless authentication, data privacy, security, robustness against attacks, easy deployment, and self-maintenance. Such features can be brought by blockchain, a technology born with a cryptocurrency called Bitcoin. In this paper, a thorough review on how to adapt blockchain to the specific needs of IoT in order to develop Blockchain-based IoT (BIoT) applications is presented. After describing the basics of blockchain, the most relevant BIoT applications are described with the objective of emphasizing how blockchain can impact traditional cloud-centered IoT applications. Then, the current challenges and possible optimizations are detailed regarding many aspects that affect the design, development, and deployment of a BIoT application. Finally, some recommendations are enumerated with the aim of guiding future BIoT researchers and developers on some of the issues that will have to be tackled before deploying the next generation of BIoT applications."}85{"work_id": 4401666902, "title": "Cyberfraud in the Nigerian Banking Sector: The Techniques and Preventive Measures", "abstract": "The cyberfraud techniques emerged as a significant threat to the banking sector in Nigeria. Bank customers have been losing their hard-earned funds to these measures due to a lack of sufficient awareness of the cyberfraud tactics and preventive measures. There has been an increased rate of loss recorded annually in the banking sector due to fraud. The banking sector in Nigeria recorded a loss to a tune amount of \u20a617.6bn in 2023 amount higher than the budget allocation for education in most states of the country. Social engineering remains the persistent and predominant technique fraudsters employ to defraud bank customers in Nigeria. Phishing, identity theft, login credential theft, ATM card swap, and skimming are other techniques cybercriminals employ. This research paper aims to examine and analyze the modus operandi of these techniques and explore the strategic preventive measures that can be implemented by customers to mitigate these risks, such as using advanced cybersecurity technologies, multi-factor authentication systems, employee training, as well as regulatory controls. The paper highlights the significance of a proactive and adaptive cybersecurity posture, continuous monitoring, and collaboration with regulatory bodies to enhance the resilience of the banking sector against cyber threats. The research focuses more on cyberfrauds targeting bank customers, and secondary data will be used for research purposes."}86{"work_id": 2790400697, "title": "A Survey on Big Data Market: Pricing, Trading and Protection", "abstract": "Big data is considered to be the key to unlocking the next great waves of growth in productivity. The amount of collected data in our world has been exploding due to a number of new applications and technologies that permeate our daily lives, including mobile and social networking applications, and Internet of Thing-based smart-world systems (smart grid, smart transportation, smart cities, and so on). With the exponential growth of data, how to efficiently utilize the data becomes a critical issue. This calls for the development of a big data market that enables efficient data trading. Via pushing data as a kind of commodity into a digital market, the data owners and consumers are able to connect with each other, sharing and further increasing the utility of data. Nonetheless, to enable such an effective market for data trading, several challenges need to be addressed, such as determining proper pricing for the data to be sold or purchased, designing a trading platform and schemes to enable the maximization of social welfare of trading participants with efficiency and privacy preservation, and protecting the traded data from being resold to maintain the value of the data. In this paper, we conduct a comprehensive survey on the lifecycle of data and data trading. To be specific, we first study a variety of data pricing models, categorize them into different groups, and conduct a comprehensive comparison of the pros and cons of these models. Then, we focus on the design of data trading platforms and schemes, supporting efficient, secure, and privacy-preserving data trading. Finally, we review digital copyright protection mechanisms, including digital copyright identifier, digital rights management, digital encryption, watermarking, and others, and outline challenges in data protection in the data trading lifecycle."}87{"work_id": 4302383326, "title": "Private Information Retrieval with Side Information", "abstract": "The objective of the classical Private Information Retrieval (PIR) problem is to enable a user to\\ndownload a message from a database that is replicated across a collection of non-colluding servers\\nwithout revealing the identity of the demanded message to the servers. In the classical PIR problem\\nthe user has no prior information about the content of messages in the database. It is easy to verify\\nin the special case of the PIR problem when there is only one server in the system, the user must\\ndownload all messages from the database in order keep information about the message they want\\nprivate.\\nIn a real environment the user may have other sources to download and obtain messages from\\nsuch as trusted peer-to-peer communication. In this way, the user has the potential to obtain some\\nof the messages that are contained in the database to use as side information in a PIR scheme with\\nthe servers. Accordingly, we introduce the Private Information Retrieval with Side Information\\n(PIR-SI) problem that focuses on settings in which the user has side information about some messages\\nin the database. To capture the different levels of privacy a user may want to enforce in\\nPIR-SI schemes two metrics of privacy, W-privacy and (W,S)-privacy, are introduced. W-privacy\\nonly protects information about the identity of the message that the user wants and is most similar\\nto the measure of privacy in the original PIR problem. (W, S)-privacy protects the identity of the\\nwanted message as well as the identities of the messages they have as side information and is a\\nstronger sense of privacy than W-privacy. When enforcing either measure of privacy the user no\\nlonger has to download all the messages in the database, even if there is only one server in the\\nsystem; side information reduces the amount of data that one has to download in a PIR scheme.\\nThe first case of the PIR-SI problem that we consider is when the user has M messages for side\\ninformation and wants a different message from the database of K messages. When there is only\\none server in the system, we show that the optimal download rate for a W-private scheme is k^-1/M+1\\nand the optimal download rate for a (W, S)-private scheme is 1/K-M.\\nWhen there is more than one\\nserver in the system a W-private scheme is presented that has a larger rate than the classical PIR\\nscheme, but its optimality is not shown.\\nThe second case of the PIR-SI problem that is considered is when the user has M messages as\\nside information, the user wants D > 1 distinct messages from the database, and there is only one\\nserver in the system. In the case when M = 1 a (W, S)-private optimal scheme is presented and\\nshown to be optimal. In the case when M \u2265 D and D = 2 a W-private scheme that can increase\\nthe rate from the (W, S)-private scheme with the same parameters is presented. This scheme\u2019s\\noptimality reminds an open problem. We highlight the difficulty of finding an optimal scheme and\\ndetermining the capacity of the multi-message PIR-SI problem."}88{"work_id": 4399027046, "title": "Secure and Privacy-preserving Federated Learning with Explainable Artificial Intelligence for Smart Healthcare System", "abstract": "The growing population around the globe has a significant impact on various sectors including the labor force, healthcare, and the global economy. The healthcare sector is among the most affected sectors by the growing population due to the increasing demand for resources including doctors, nurses, equipment, and healthcare facilities. Intelligent systems have been incorporated to enhance decision-making, management, prognosis, and diagnosis in order to tackle such issues and offer improved healthcare to patients. Among such systems, those based on deep learning (DL), a subclass of machine learning (ML) have outperformed many traditional statistical and ML systems owing to their capability of automatically discovering and learning related features for a given task and robustness. Therefore, the use of DL has seen a steady increase in many applications. Nevertheless, usually, the training of DL models relies on a single centralized server, which brings many challenges: (1) except for some big enterprises most of the small enterprises have limited quality data, which is insufficient to support the training of data-hungry DL models, (2) access to data, which is vital for these systems, often raises privacy concerns. The collection and analysis of sensitive patient information must be done in a secure and ethical manner to ensure the protection of individual privacy rights, (3) high communication cost and computation resources required, (4) a large number of trainable parameters make the outcome of DL hard to explain, which is required in some applications, such as healthcare. Compared to centralized ML, federated learning (FL) improves both privacy and communication costs, where clients collaboratively train a joint model without sharing the raw data directly. FL minimizes privacy breaches and safeguards sensitive data by keeping it distributed locally. This enables collaborative model training while reducing the risk of unauthorized access and data breaches. Additionally, it promotes data diversity and scalability by involving multiple sources in joint model training and decreases communication costs by sharing only model updates instead of the entire dataset. However, FL brings its own challenges. For example, heterogeneous local data among the clients makes it challenging to train a high-performing and robust global model. Sharing updates (hundreds of thousands of parameters) still has high communication costs. Additionally, the distributed nature and access control of local data in FL make it more vulnerable to malicious attacks. Moreover, the challenge of explaining the results of DL still remains challenging, and methods are needed to be developed to bring trust accountability, and transparency in sensitive applications, such as healthcare. Therefore, the aim of this thesis is to create robust frameworks that are secure, high-performing, and privacy-friendly within federated settings. These frameworks will be specifically designed for end-to-end (we train our frameworks using raw data without any manual feature extraction) healthcare applications, considering the presence of non-identically distributed data among clients in FL to bring robustness. By addressing these challenges, the objective is to enhance the overall system's resilience and effectiveness. We also propose a methodology for detecting anomalies within federated settings, particularly in applications with limited available data for the abnormal class. Furthermore, clients in FL are usually resource-constrained with limited computation and communication resources available. Therefore, to support efficient computation and communication in a federated setting we propose a lightweight framework (in terms of the trainable number of parameters). Additionally, to provide explanations of the DL models' outcomes, which are usually hard to explain because of the large number of parameters, we propose model-agnostic explainable AI modules to help explain the results of DL models. Moreover, in order to protect the proposed frameworks against cyber attacks, such as poisoning attacks, we propose a framework in federated settings, which makes the proposed healthcare frameworks more secure and trustworthy. Finally, with experimental analysis using baseline datasets for one of the most common health conditions i.e., cardiovascular diseases (arrhythmia detection, ECG anomaly detection) and human activity recognition (used for supplementing cardiovascular diseases detection), we show the superiority of the proposed frameworks over state-of-the-art work."}89{"work_id": 1497727960, "title": "FLINT : Fast library for number theory", "abstract": "Alternative version: Published as: Hart, W. B. (2010). Fast library for number theory : an introduction. Presented at: 3rd International Congress on Mathematical Software, Kobe, Japan, 13-17 September 2010. http://wrap.warwick.ac.uk/id/eprint/41629"}90{"work_id": 2915352631, "title": "The Transport Layer Security (TLS) Protocol Version 1.3", "abstract": "This document specifies version 1.3 of the Transport Layer Security\n(TLS) protocol. TLS allows client/server applications to communicate\nover the Internet in a way that is designed to prevent eavesdropping,\ntampering, and message forgery. This document updates RFCs 4492, 5705,\nand 6066 and it obsoletes RFCs 5077, 5246, and 6961. This document\nalso specifies new requirements for TLS 1.2 implementations."}91{"work_id": 4387105517, "title": "Heterogeneous Federated Learning: State-of-the-art and Research Challenges", "abstract": "Federated learning (FL) has drawn increasing attention owing to its potential use in large-scale industrial applications. Existing FL works mainly focus on model homogeneous settings. However, practical FL typically faces the heterogeneity of data distributions, model architectures, network environments, and hardware devices among participant clients. Heterogeneous Federated Learning (HFL) is much more challenging, and corresponding solutions are diverse and complex. Therefore, a systematic survey on this topic about the research challenges and state-of-the-art is essential. In this survey, we firstly summarize the various research challenges in HFL from five aspects: statistical heterogeneity, model heterogeneity, communication heterogeneity, device heterogeneity, and additional challenges. In addition, recent advances in HFL are reviewed and a new taxonomy of existing HFL methods is proposed with an in-depth analysis of their pros and cons. We classify existing methods from three different levels according to the HFL procedure: data-level, model-level, and server-level. Finally, several critical and promising future research directions in HFL are discussed, which may facilitate further developments in this field. A periodically updated collection on HFL is available at https://github.com/marswhu/HFL_Survey."}92{"work_id": 4406302454, "title": "Security and Privacy Challenges of Large Language Models: A Survey", "abstract": "Large language models (LLMs) have demonstrated extraordinary capabilities and contributed to multiple fields, such as generating and summarizing text, language translation, and question-answering. Today, LLMs have become quite popular tools in natural language processing tasks, with the capability to analyze complicated linguistic patterns and provide relevant responses depending on the context. While offering significant advantages, these models are also vulnerable to security and privacy attacks, such as jailbreaking attacks, data poisoning attacks, and personally identifiable information leakage attacks. This survey provides a thorough review of the security and privacy challenges of LLMs, along with the application-based risks in various domains, such as transportation, education, and healthcare. We assess the extent of LLM vulnerabilities, investigate emerging security and privacy attacks against LLMs, and review potential defense mechanisms. Additionally, the survey outlines existing research gaps and highlights future research directions."}93{"work_id": 3156129242, "title": "A Survey on Blockchain Technology: Evolution, Architecture and Security", "abstract": "Blockchain is a revolutionary technology that is making a great impact on modern society due to its transparency, decentralization, and security properties. Blockchain gained considerable attention due to its very first application of Cryptocurrencies e.g., Bitcoin. In the near future, Blockchain technology is determined to transform the way we live, interact, and perform businesses. Recently, academics, industrialists, and researchers are aggressively investigating different aspects of Blockchain as an emerging technology. Unlike other Blockchain surveys focusing on either its applications, challenges, characteristics, or security, we present a comprehensive survey of Blockchain technology\u2019s evolution, architecture, development frameworks, and security issues. We also present a comparative analysis of frameworks, classification of consensus algorithms, and analysis of security risks & cryptographic primitives that have been used in the Blockchain so far. Finally, this paper elaborates on key future directions, novel use cases and open research challenges, which could be explored by researchers to make further advances in this field."}94{"work_id": 2912977751, "title": "A Blockchain-Based Nonrepudiation Network Computing Service Scheme for Industrial IoT", "abstract": "Emerging network computing technologies extend the functionalities of industrial IoT (IIoT) terminals. However, this promising service-provisioning scheme encounters problems in untrusted and distributed IIoT scenarios because malicious service providers or clients may deny service provisions or usage for their own interests. Traditional nonrepudiation solutions fade in IIoT environments due to requirements of trusted third parties or unacceptable overheads. Fortunately, the blockchain revolution facilitates innovative solutions. In this paper, we propose a blockchain-based fair nonrepudiation service provisioning scheme for IIoT scenarios in which the blockchain is used as a service publisher and an evidence recorder. Each service is separately delivered via on-chain and off-chain channels with mandatory evidence submissions for nonrepudiation purpose. Moreover, a homomorphic-hash-based service verification method is designed that can function with mere on-chain evidence. And an impartial smart contract is implemented to resolve disputes. The security analysis demonstrates the dependability, and the evaluations reveal the effectiveness and efficiency."}95{"work_id": 4317737920, "title": "Blockchain Technology: Security Issues, Healthcare Applications, Challenges and Future Trends", "abstract": "Blockchain technology provides a data structure with inherent security properties that include cryptography, decentralization, and consensus, which ensure trust in transactions. It covers widely applicable usages, such as in intelligent manufacturing, finance, the Internet of things (IoT), medicine and health, and many different areas, especially in medical health data security and privacy protection areas. Its natural attributes, such as contracts and consensus mechanisms, have leading-edge advantages in protecting data confidentiality, integrity, and availability. The security issues are gradually revealed with in-depth research and vigorous development. Unlike traditional paper storage methods, modern medical records are stored electronically. Blockchain technology provided a decentralized solution to the trust-less issues between distrusting parties without third-party guarantees, but the \u201ctrust-less\u201d security through technology was easily misunderstood and hindered the security differences between public and private blockchains appropriately. The mentioned advantages and disadvantages motivated us to provide an advancement and comprehensive study regarding the applicability of blockchain technology. This paper focuses on the healthcare security issues in blockchain and sorts out the security risks in six layers of blockchain technology by comparing and analyzing existing security measures. It also explores and defines the different security attacks and challenges when applying blockchain technology, which promotes theoretical research and robust security protocol development in the current and future distributed work environment."}96{"work_id": 2905867785, "title": "A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks", "abstract": "The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and industries. The blockchain network was originated from the Internet financial sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and data-driven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability, and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this paper, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of decentralized consensus in blockchain networks, our in-depth review of the state-of-the-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review of the strategy adopted for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey of the emerging applications of blockchain networks in a broad area of telecommunication. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions."}97{"work_id": 2231944250, "title": "Enigma: Decentralized Computation Platform with Guaranteed Privacy", "abstract": "A peer-to-peer network, enabling different parties to jointly store and run computations on data while keeping the data completely private. Enigma's computational model is based on a highly optimized version of secure multi-party computation, guaranteed by a verifiable secret-sharing scheme. For storage, we use a modified distributed hashtable for holding secret-shared data. An external blockchain is utilized as the controller of the network, manages access control, identities and serves as a tamper-proof log of events. Security deposits and fees incentivize operation, correctness and fairness of the system. Similar to Bitcoin, Enigma removes the need for a trusted third party, enabling autonomous control of personal data. For the first time, users are able to share their data with cryptographic guarantees regarding their privacy."}98{"work_id": 2927344969, "title": "Global Blockchain Benchmarking Study", "abstract": null}99{"work_id": 7159977201, "title": "AsyncFed-SAR: Toward Communication-Efficient Asynchronous Federated Learning for SAR Image Target Recognition", "abstract": null}100{"work_id": 7125492275, "title": "Source-free domain adaptation for cross-domain remaining useful life prediction: A distributed federated learning perspective", "abstract": null}101{"work_id": 3209866323, "title": "Separable Reversible Data Hiding in an Encrypted Image", "abstract": null}102{"work_id": 7121318593, "title": "A comprehensive survey on federated learning for privacy preservation in digital healthcare applications", "abstract": null}103{"work_id": 4365934498, "title": "An Efficient Key Recovery Attack on SIDH", "abstract": null}104{"work_id": 4205766150, "title": "Tracking and tracing COVID: Protecting privacy and data while using apps and biometrics", "abstract": null}105{"work_id": 4408476406, "title": "CBRFL: A framework for Committee-based Byzantine-Resilient Federated Learning", "abstract": null}106{"work_id": 4353004773, "title": "IEEE Symposium on Security and Privacy", "abstract": null}107{"work_id": 3196371845, "title": "Federated learning on non-IID data: A survey", "abstract": null}108{"work_id": 4293155325, "title": "Federated learning review: Fundamentals, enabling technologies, and future applications", "abstract": null}109{"work_id": 2910044495, "title": "Key Agreement with Physical Unclonable Functions and Biometric Identifiers", "abstract": null}110{"work_id": 2961501274, "title": "Aleksander Berentsen Recommends \u201cBitcoin: A Peer-to-Peer Electronic Cash System\u201d by Satoshi Nakamoto", "abstract": null}111{"work_id": 4407318675, "title": "Federated learning-empowered smart manufacturing and product lifecycle management: A review", "abstract": null}112{"work_id": 7118779462, "title": "Federated learning in healthcare: Recent progress and challenges", "abstract": null}113{"work_id": 2800028583, "title": "Blockchain Technology Use Cases in Healthcare", "abstract": null}114{"work_id": 2797770018, "title": "A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment", "abstract": null}115{"work_id": 4406269727, "title": "Federated learning framework based on trimmed mean aggregation rules", "abstract": null}116{"work_id": 4248175462, "title": "Bitcoin: A Peer-to-Peer Electronic Cash System", "abstract": null}117{"work_id": 2803836845, "title": "Bitcoin's Growing Energy Problem", "abstract": null}118{"work_id": 2789828921, "title": "Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization", "abstract": null}119{"work_id": 3013489453, "title": "On the responsible use of digital data to tackle the COVID-19 pandemic", "abstract": null}120{"work_id": 4301785137, "title": "The Illusion of Topological Compression: Why Persistent Homology Fails as a Real-Time Prompt-Injection Defense", "abstract": null}121{"work_id": 3022665000, "title": "COVID-19 immunity passports and vaccination certificates: scientific, equitable, and legal challenges", "abstract": null}122{"work_id": 3100779497, "title": "Federated Learning for Healthcare Informatics", "abstract": null}123{"work_id": 2905514290, "title": "An information security risk management gap analysis tool based on ISO/IEC 27005:2011 compliance for SMEs in Kenya", "abstract": null}124{"work_id": 2910369831, "title": "Blockchain based searchable encryption for electronic health record sharing", "abstract": null}125{"work_id": 7160693558, "title": "Passivity-based synchronization of Markovian jump inertial neural networks via adaptive event-driven protocol and its application to image encryption", "abstract": null}126