1openalex_id,contribution_type,description,judged_from,bib_key22915352631,engineering,RFC 8446: specification of TLS 1.3 (IETF standards track); the most cited applied-cryptography document of the baseline period,abstract,rescorla2018tls1332913851998,survey,NIST IR 8240: status report on the first round of the post-quantum standardisation process,abstract,nist2019pqcround144226075590,survey,NIST IR 8413: third-round status report announcing the post-quantum selections (July 2022),abstract,nist2022pqcround354401459825,engineering,FIPS 203: ML-KEM standard; security related to Module-LWE; 'believed to be secure' against quantum adversaries,abstract,nist2024fips20364401442713,engineering,FIPS 204: ML-DSA digital signature standard,abstract,nist2024fips20474365934498,method,Key-recovery attack on SIDH (title only; no abstract in the record),metadata,castryck2023sidh84293168393,theory,2022 ACM reprint of Diffie and Hellman's 1976 New Directions in Cryptography; the record's abstract is the landing page (artefact),metadata,92916447643,engineering,2026 duplicate record of TLS 1.3 whose abstract describes an earlier protocol version (artefact),metadata,104214667959,survey,"Survey of blockchain technology and its security: consensus comparison, cryptographic components, risk analysis",abstract,guo2022blockchainsecurity112970408908,method,Deep Leakage from Gradients: gradient inversion attack on federated updates (2020 LNCS reprint; no abstract in the record),metadata,zhu2020dlg127154469464,method,Cryptanalysis of a chaos-based video cryptosystem; reports lack of standardisation in that sub-literature,abstract,134413785573,engineering,Video segment encryption with a memristive neuron model (chaos-based sub-literature),abstract,147134847443,engineering,Hyperchaos-based image encryption for consumer IoT (chaos-based sub-literature),abstract,157124450903,survey,Survey of 203 papers on security and privacy attacks and defences in federated learning (Information Fusion 2026),abstract,jimenezgutierrez2026flsecurity161598516026,empirical,2026 ResearchHub copy of the 2010 Scantegrity II municipal election case study (artefact),abstract,171497727960,engineering,2026 repository record of the 2010 FLINT number-theory library (artefact),abstract,184300127641,engineering,Hyperledger Fabric: modular open-source permissioned blockchain system (EuroSys 2018),abstract,androulaki2018fabric192905867785,survey,Survey of consensus mechanisms and mining strategy management in blockchain networks,abstract,wang2019consensus202892520153,survey,NIST IR 8202 blockchain technology overview,abstract,yaga2018blockchain213123868249,empirical,Estimate of illegal activity financed through bitcoin (Review of Financial Studies),abstract,foley2019bitcoin223122636688,theory,"Theory of blockchain disruption, decentralised consensus and smart contracts (Review of Financial Studies)",abstract,cong2019smartcontracts232784387076,theory,Blockchains as an institutional technology (Journal of Institutional Economics),abstract,davidson2018institutions242898069736,survey,Blockchain and sustainable supply chain management (critical examination),abstract,saberi2019supplychain252606010969,survey,Blockchains for business process management: challenges and seven research directions,abstract,mendling2018bpm263123722958,method,Ontology-driven blockchain design for supply-chain provenance,abstract,kim2018provenance272965761488,survey,Blockchain in agriculture and food supply chains: projects and barriers,abstract,282914735843,survey,"Blockchain-enabled smart contracts: architecture, applications and trends",abstract,294320717855,survey,Blockchain for the metaverse: review (763 citations in 36 months),abstract,huynh2023metaverse303162358200,survey,"NFT overview, evaluation, opportunities and challenges (arXiv preprint)",abstract,wang2021nft314289548059,method,"Datalog-based detection of cross-function reentrancy from EVM traces, extending TxSpector (FMBC 2026)",abstract,guesmi2026reentrancy324211068006,theory,"Bitcoin whitepaper, SSRN record dated 2022 (duplicate of nakamoto2008bitcoin; artefact)",metadata,nakamoto2008bitcoin334248175462,theory,"Bitcoin whitepaper, SSRN record dated 2019 (duplicate; artefact)",metadata,nakamoto2008bitcoin344405432346,theory,"Bitcoin whitepaper, SSRN record dated 2024 (duplicate; artefact)",metadata,nakamoto2008bitcoin352460937040,empirical,Adversarial examples survive physical-world capture (2018 chapter reprint of the 2017 paper),abstract,kurakin2018physical362789828921,benchmark,CIC-IDS2017 intrusion-detection dataset and traffic characterisation (no abstract in the record),metadata,372624307925,survey,Survey of security and privacy issues of Bitcoin (IEEE COMST),abstract,conti2018bitcoinsecurity382805930283,survey,Review of blockchain for the Internet of Things (IEEE Access),abstract,fernandez2018biot392795085730,survey,Blockchain for healthcare cloud data security: position paper,abstract,esposito2018healthcare402231944250,engineering,Enigma: decentralised secure-computation platform (2018 MIT Press chapter; belongs to privacy-preserving computation),abstract,414406302454,survey,"Survey of security and privacy challenges of LLMs: jailbreaking, poisoning, PII leakage (ACM CSUR)",abstract,das2025llmsecurity427129030749,survey,"Model Context Protocol: lifecycle (4 phases, 16 activities) and security threat taxonomy (ACM TOSEM 2026)",abstract,hou2026mcp437130652904,method,"Vul-RAG: knowledge-level RAG for LLM vulnerability detection; base LLM accuracy 0.06-0.14, +16-24 points",abstract,du2026vulrag444387210439,engineering,HuntGPT: ML anomaly detection with XAI and an LLM interface for threat hunting,abstract,ali2026huntgpt457125967353,method,Fine-tuned RoBERTa phishing-email detector with explainability,abstract,uddin2026phishing464301785137,method,"2026 SSRN preprint arguing persistent homology fails as a prompt-injection defence (no abstract; not peer-reviewed; NCS36 9,806)",metadata,477139094415,method,Fedformer (time-series forecasting Transformer) misclassified as cybersecurity (artefact),abstract,484353004773,survey,Venue-level record 'IEEE Symposium on Security and Privacy' (IT Professional 2023) counted as a work (artefact),metadata,492912213068,theory,"Federated Machine Learning: horizontal, vertical and transfer FL framework (ACM TIST; most cited technical paper of the area)",abstract,yang2019fml503018102029,method,SplitFed: combination of split learning and federated learning (AAAI 2022),abstract,thapa2022splitfed517137155252,survey,Systematic review of differentially private federated learning (ACM CSUR 2026),abstract,fu2026dpfl524412565294,survey,Survey of machine unlearning and the right to be forgotten (ACM TIST 2025),abstract,nguyen2025unlearning534414891672,survey,Scoping review of differential privacy in medical deep learning (74 studies; epsilon trade-offs),abstract,mohammadi2026dpmedical543036422000,survey,Survey of COVID-19 contact-tracing apps (IEEE Access 2020),abstract,ahmed2020tracingapps553014084098,theory,Privacy analysis of contact-tracing apps (TraceTogether); arXiv preprint,abstract,cho2020tracing564302383326,theory,Private information retrieval with side information (dissertation),abstract,574408302972,empirical,AI and students' academic development (education; misclassified into privacy-preserving computation),abstract,584407111324,survey,AI and student well-being mini-review (misclassified),abstract,597127055292,survey,AI in higher education systematic review (misclassified),abstract,604406636601,empirical,AI in auditing: 22 interviews (misclassified),abstract,613045674654,empirical,Federated learning in medicine: multi-institutional collaboration without sharing patient data (Sci. Rep. 2020),abstract,sheller2020flmedicine623018464563,method,FedHealth: federated transfer learning for wearable healthcare,abstract,chen2020fedhealth633100779497,survey,Federated learning for healthcare informatics (no abstract in the record),metadata,xu2021flhealth642995735216,method,q-FFL: fair resource allocation objective for federated learning (ICLR 2020; record without DOI),abstract,li2019qffl653196371845,survey,Federated learning on non-IID data: survey (no abstract in the record),metadata,zhu2021noniid663123459983,survey,A survey on federated learning (KBS 2021; no abstract in the record),metadata,zhang2021flsurvey673133814152,survey,Towards personalized federated learning: survey of PFL under heterogeneity (IEEE TNNLS),abstract,tan2023personalizedfl684382463479,method,FedALA: adaptive local aggregation for personalised FL (AAAI 2023),abstract,zhang2023fedala694387105517,survey,"Heterogeneous federated learning: data, model, network and hardware heterogeneity (ACM CSUR)",abstract,ye2024heterogeneousfl704393156671,method,FedTGP: trainable global prototypes for model-heterogeneous FL (AAAI 2024),abstract,zhang2024fedtgp714402716118,method,FedAS: addressing intra- and inter-client inconsistency in personalised FL (CVPR 2024),abstract,yang2024fedas724415878254,method,DFCA: decentralised federated clustering without a central server (IEEE IoTJ 2026),abstract,734408476406,method,CBRFL: committee-based Byzantine-resilient federated learning (no abstract in the record),metadata,xu2025cbrfl744412162472,survey,Scoping review of the governance of federated learning in healthcare (npj Digital Medicine 2025),abstract,eden2025flgovernance757133255133,survey,Survey of six core challenges of federated learning (2026),abstract,76