1work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,ntitle,score,set,pi_review22905514290,,,An information security risk management gap analysis tool based on ISO/IEC 27005:2011 compliance for SMEs in Kenya,,2018,dissertation,cybersecurity,1,63.0,83.0,849.0759849906191,95,,False,False,baseline,aninformationsecurityriskmanagementgapanalysistoolbasedonisoiec270052011complianceforsmesinkenya,849.0759849906191,landmark,True32460937040,10.1201/9781351251389-8,,Adversarial Examples in the Physical World,Chapman & Hall/CRC eBooks,2018,book-chapter,cybersecurity,4,805.0,1507.0,699.9376680212566,4388,809.1968,True,True,baseline,adversarialexamplesinthephysicalworld,699.9376680212566,landmark,True44301785137,10.2139/ssrn.7475259,,The Illusion of Topological Compression: Why Persistent Homology Fails as a Real-Time Prompt-Injection Defense,SSRN Electronic Journal,2026,preprint,cybersecurity,2,252.0,252.0,9805.894736842105,252,,False,True,post,theillusionoftopologicalcompressionwhypersistenthomologyfailsasarealtimepromptinjectiondefense,638.9385438721052,landmark,True57129030749,10.1145/3796519,,"Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions",ACM Transactions on Software Engineering and Methodology,2026,article,cybersecurity,4,178.0,178.0,198.07867267589847,149,498.5414,True,True,post,modelcontextprotocolmcplandscapesecuritythreatsandfutureresearchdirections,451.31373336997916,landmark,True63188417193,,,Machine learning for offensive cyber operations,NORA - Norwegian Open Research Archives,2023,dissertation,cybersecurity,6,43.0,51.0,424.2320328542094,58,,True,True,post,machinelearningforoffensivecyberoperations,424.2320328542094,landmark,True74289548059,10.4230/oasics.fmbc.2026.8,,Detecting Cross-Function Reentrancy from EVM Traces,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,cybersecurity,5,148.0,148.0,1051.4722907203115,148,9.1528,True,True,post,detectingcrossfunctionreentrancyfromevmtraces,375.24962100425233,landmark,True83201596224,,,ELLIPTIC CURVE DIGITAL SIGNATURE ALGORITHM,NC Digital Online Collection of Knowledge and Scholarship (The University of North Carolina at Greensboro),2021,dissertation,cybersecurity,2,27.0,28.0,308.9145728643216,35,,True,False,baseline,ellipticcurvedigitalsignaturealgorithm,308.9145728643216,landmark,True9652269645,10.57709/1059372,,A Design and Analysis of Graphical Password,Digital Archive @ GSU,2022,dissertation,cybersecurity,1,35.0,35.0,303.5964912280702,43,,True,True,baseline,adesignandanalysisofgraphicalpassword,303.5964912280702,landmark,True102231944250,10.7551/mitpress/11636.003.0018,,Enigma: Decentralized Computation Platform with Guaranteed Privacy,The MIT Press eBooks,2018,book-chapter,cybersecurity,6,169.0,230.0,270.2898179020262,368,204.4551,True,True,baseline,enigmadecentralizedcomputationplatformwithguaranteedprivacy,270.2898179020262,landmark,True113041344359,,,Machine Learning for anomaly detection in IoT networks :Malware analysis on the IoT-23 data set,,2020,dissertation,cybersecurity,4,8.0,31.0,265.4812030075188,64,,True,False,baseline,machinelearningforanomalydetectioniniotnetworksmalwareanalysisontheiot23dataset,265.4812030075188,landmark,True124401666902,10.38124/ijisrt/ijisrt24aug395,,Cyberfraud in the Nigerian Banking Sector: The Techniques and Preventive Measures,International Journal of Innovative Science and Research Technology (IJISRT),2024,article,cybersecurity,1,739.0,739.0,222.49445823978112,739,282.4198,True,True,post,cyberfraudinthenigerianbankingsectorthetechniquesandpreventivemeasures,222.49445823978112,landmark,True134353004773,10.1109/mitp.2023.3246667,,IEEE Symposium on Security and Privacy,IT Professional,2023,article,cybersecurity,2,671.0,853.0,199.92253679509537,1167,130.8885,False,True,post,ieeesymposiumonsecurityandprivacy,199.92253679509537,landmark,True142805930283,10.1109/access.2018.2842685,2402.00687,A Review on the Use of Blockchain for the Internet of Things,IEEE Access,2018,article,cybersecurity,5,184.0,370.0,196.1729236493096,1078,221.3514,True,True,post,areviewontheuseofblockchainfortheinternetofthings,196.1729236493096,landmark,True152624307925,10.1109/comst.2018.2842460,1706.00916,A Survey on Security and Privacy Issues of Bitcoin,IEEE Communications Surveys & Tutorials,2018,article,cybersecurity,6,207.0,367.0,194.58233237647738,1103,211.3082,True,True,pre,asurveyonsecurityandprivacyissuesofbitcoin,194.58233237647738,landmark,True164392771733,,,Sécurisation des Algorithmes d'Apprentissage Automatique,theses.fr (ABES),2023,dissertation,cybersecurity,5,17.0,21.0,174.68377823408625,21,,True,True,post,scurisationdesalgorithmesdapprentissageautomatique,174.68377823408625,landmark,True172789828921,10.5220/0006639801080116,,Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization,International Conference on Information Systems Security and Privacy (ICISSP),2018,conference-paper,cybersecurity,2,200.0,534.0,162.76665116819714,4757,217.8381,False,True,baseline,towardgeneratinganewintrusiondetectiondatasetandintrusiontrafficcharacterization,162.76665116819714,landmark,True184406302454,10.1145/3712001,2402.00888,Security and Privacy Challenges of Large Language Models: A Survey,ACM Computing Surveys,2025,article,cybersecurity,5,357.0,357.0,121.61180437793685,357,141.8982,True,True,post,securityandprivacychallengesoflargelanguagemodelsasurvey,155.99884965417968,landmark,True192309315845,10.58088/javg-d166,,Human-centric training and assessment for cyber situation awareness,"Library, Museums and Press - UDSpace (University of Delaware)",2024,dissertation,cybersecurity,4,4.0,4.0,155.76296296296297,4,,True,True,post,humancentrictrainingandassessmentforcybersituationawareness,155.76296296296297,landmark,True202795085730,10.1109/mcc.2018.011791712,,Blockchain: A Panacea for Healthcare Cloud-Based Data Security and Privacy?,IEEE Cloud Computing,2018,article,cybersecurity,6,130.0,270.0,143.1532145549016,855,159.8872,True,False,baseline,blockchainapanaceaforhealthcarecloudbaseddatasecurityandprivacy,143.1532145549016,landmark,True212984937325,,,Trusted Execution Environments in Cloud Computing,Aaltodoc (Aalto University),2021,dissertation,cybersecurity,5,22.0,31.0,140.99890109890111,42,,True,False,baseline,trustedexecutionenvironmentsincloudcomputing,140.99890109890111,landmark,True222946416612,,,Inadvertently Making Cyber Criminals Rich: A Comprehensive Study of Cryptojacking Campaigns at Internet Scale,Research Repository (Delft University of Technology),2019,dissertation,cybersecurity,2,9.0,14.0,140.31616982836496,19,,True,True,baseline,inadvertentlymakingcybercriminalsrichacomprehensivestudyofcryptojackingcampaignsatinternetscale,140.31616982836496,landmark,True231550000763,10.1184/r1/6621806,,Crying Wolf: An Empirical Study of SSL Warning Effectiveness,KiltHub Repository,2018,article,cybersecurity,3,245.0,262.0,138.91163782734898,331,29.7278,True,True,baseline,cryingwolfanempiricalstudyofsslwarningeffectiveness,138.91163782734898,landmark,True242781723315,10.1109/sp.2019.00002,1801.01203,Spectre Attacks: Exploiting Speculative Execution,Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy,2019,conference-paper,cybersecurity,7,693.0,1001.0,133.14175590649612,1780,109.082,True,True,baseline,spectreattacksexploitingspeculativeexecution,133.14175590649612,landmark,True254322207773,10.26686/wgtn.22180858,,"Automating Behavior-based Ransomware Analysis, Detection, and Classification Using Machine Learning",,2023,dissertation,cybersecurity,7,9.0,10.0,132.5,11,,True,True,post,automatingbehaviorbasedransomwareanalysisdetectionandclassificationusingmachinelearning,132.5,landmark,True264392353733,10.1016/j.hcc.2024.100211,2312.02003,"A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly",High-Confidence Computing,2024,article,cybersecurity,7,728.0,1059.0,131.53622644511728,1059,157.601,True,True,post,asurveyonlargelanguagemodelllmsecurityandprivacythegoodthebadandtheugly,131.53622644511728,landmark,True271598516026,10.55277/researchhub.8k9hq1lf.1,,Scantegrity II Municipal Election at Takoma Park: The First E2E Binding Governmental Election with Ballot Privacy,,2026,report,applied_cryptography,2,97.0,97.0,914.8011049723757,107,,True,True,post,scantegrityiimunicipalelectionattakomaparkthefirste2ebindinggovernmentalelectionwithballotprivacy,245.94062998251673,landmark,True284293168393,10.1145/3549993.3550007,,New Directions in Cryptography,ACM eBooks,2022,book-chapter,applied_cryptography,1,316.0,402.0,215.4337868090236,668,135.2339,True,False,baseline,newdirectionsincryptography,215.4337868090236,landmark,True291497727960,10.82444/warw.33628720,,FLINT : Fast library for number theory,Warwick Research Archive Portal (University of Warwick),2026,article,applied_cryptography,1,59.0,59.0,166.59291493012995,60,0.0,True,True,post,flintfastlibraryfornumbertheory,149.59275431926275,landmark,True307134847443,10.1109/tce.2026.3672135,,"3D Non-Degenerate Hyperchaos: Design, Analysis, and Application in Image Encryption",IEEE Transactions on Consumer Electronics,2026,article,applied_cryptography,4,59.0,59.0,158.14163483575248,59,183.8619,True,False,post,3dnondegeneratehyperchaosdesignanalysisandapplicationinimageencryption,149.59275431926275,landmark,True311516024962,10.7275/409838,,Energy Efficient Adaptive Reed-Solomon Decoding System,Scholarworks (University of Massachusetts Amherst),2023,dissertation,applied_cryptography,3,8.0,8.0,146.84444444444443,9,,True,True,post,energyefficientadaptivereedsolomondecodingsystem,146.84444444444443,landmark,True327154469464,10.1145/3808699,,Cryptanalysis and Improvement of a Video Cryptosystem via Chaos and S-Box,ACM Transactions on Multimedia Computing Communications and Applications,2026,article,applied_cryptography,3,57.0,57.0,152.78090145148968,57,177.6293,True,True,post,cryptanalysisandimprovementofavideocryptosystemviachaosandsbox,144.52181349488094,landmark,True334220942018,10.1201/9781003187165-10,,Public and private blockchains,CRC Press eBooks,2022,book-chapter,applied_cryptography,2,166.0,175.0,135.97268806899856,179,35.3352,True,False,baseline,publicandprivateblockchains,135.97268806899856,landmark,True342910044495,,,Key Agreement with Physical Unclonable Functions and Biometric Identifiers,mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich),2019,dissertation,applied_cryptography,3,6.0,17.0,116.57142857142857,21,,False,False,baseline,keyagreementwithphysicalunclonablefunctionsandbiometricidentifiers,116.57142857142857,landmark,True352915352631,10.17487/rfc8446,,The Transport Layer Security (TLS) Protocol Version 1.3,,2018,report,applied_cryptography,3,316.0,486.0,110.30219435736677,1851,,True,False,baseline,thetransportlayersecuritytlsprotocolversion13,110.30219435736677,landmark,True364396670109,10.4018/979-8-3693-1335-0.ch012,,Advanced Digital Data Processing Using Cloud Cryptography,Advances in civil and industrial engineering book series,2024,book-chapter,applied_cryptography,4,133.0,133.0,86.71235909822867,133,97.3697,True,False,post,advanceddigitaldataprocessingusingcloudcryptography,86.71235909822867,landmark,True374214667959,10.1016/j.bcra.2022.100067,,A survey on blockchain technology and its security,Blockchain Research and Applications,2022,article,applied_cryptography,4,164.0,337.0,82.8248106660309,571,120.3925,True,True,baseline,asurveyonblockchaintechnologyanditssecurity,82.8248106660309,landmark,True384226075590,10.6028/nist.ir.8413,,Status report on the third round of the NIST Post-Quantum Cryptography Standardization process,,2022,report,applied_cryptography,3,144.0,242.0,79.32594644506003,395,,True,True,baseline,statusreportonthethirdroundofthenistpostquantumcryptographystandardizationprocess,79.32594644506003,landmark,True394413785573,10.1109/tdsc.2025.3603570,,Encrypt a Story: A Video Segment Encryption Method Based on the Discrete Sinusoidal Memristive Rulkov Neuron,IEEE Transactions on Dependable and Secure Computing,2025,article,applied_cryptography,4,176.0,176.0,57.549674211696974,176,64.4002,True,False,post,encryptastoryavideosegmentencryptionmethodbasedonthediscretesinusoidalmemristiverulkovneuron,76.90699590794293,landmark,True402910369831,10.1016/j.future.2019.01.018,,Blockchain based searchable encryption for electronic health record sharing,Future Generation Computer Systems,2019,article,applied_cryptography,5,84.0,167.0,68.16664743486187,482,87.1622,False,False,baseline,blockchainbasedsearchableencryptionforelectronichealthrecordsharing,68.16664743486187,landmark,True414365934498,10.1007/978-3-031-30589-4_15,,An Efficient Key Recovery Attack on SIDH,Lecture notes in computer science,2023,conference-paper,applied_cryptography,1,145.0,223.0,67.25882266726688,281,73.5527,False,True,post,anefficientkeyrecoveryattackonsidh,67.25882266726688,landmark,True422913851998,10.6028/nist.ir.8240,,Status report on the first round of the NIST post-quantum cryptography standardization process,,2019,report,applied_cryptography,2,70.0,153.0,65.49329758713137,363,,True,True,baseline,statusreportonthefirstroundofthenistpostquantumcryptographystandardizationprocess,65.49329758713137,landmark,True433209866323,10.18063/wct.v0i0.407,,Separable Reversible Data Hiding in an Encrypted Image,Wireless Communication Technology,2018,article,applied_cryptography,3,323.0,372.0,65.312839313152,427,26.419,False,False,baseline,separablereversibledatahidinginanencryptedimage,65.312839313152,landmark,True444406028284,10.1109/jiot.2025.3525623,,A Novel Dynamic Hill Cipher and Its Applications on Medical IoT,IEEE Internet of Things Journal,2025,article,applied_cryptography,4,147.0,147.0,48.06705743817872,147,53.7888,True,False,post,anoveldynamichillcipheranditsapplicationsonmedicaliot,64.2348204458387,landmark,True453022665000,10.1016/s0140-6736(20)31034-5,,"COVID-19 immunity passports and vaccination certificates: scientific, equitable, and legal challenges",The Lancet,2020,article,applied_cryptography,1,158.0,218.0,63.47984296349881,291,61.3673,False,True,baseline,covid19immunitypassportsandvaccinationcertificatesscientificequitableandlegalchallenges,63.47984296349881,landmark,True463156129242,10.1109/access.2021.3072849,,"A Survey on Blockchain Technology: Evolution, Architecture and Security",IEEE Access,2021,article,applied_cryptography,4,87.0,226.0,59.84604799688895,522,84.4336,True,True,baseline,asurveyonblockchaintechnologyevolutionarchitectureandsecurity,59.84604799688895,landmark,True4744936433,10.1145/3335741.3335755,,"How to play any mental game, or a completeness theorem for protocols with honest majority",ACM eBooks,2019,book-chapter,applied_cryptography,2,143.0,189.0,58.79166858882687,403,47.6938,True,False,baseline,howtoplayanymentalgameoracompletenesstheoremforprotocolswithhonestmajority,58.79166858882687,landmark,True484317737920,10.3390/electronics12030546,,"Blockchain Technology: Security Issues, Healthcare Applications, Challenges and Future Trends",Electronics,2023,article,applied_cryptography,7,136.0,241.0,56.48456197845015,282,66.2667,True,True,post,blockchaintechnologysecurityissueshealthcareapplicationschallengesandfuturetrends,56.48456197845015,landmark,True492912977751,10.1109/tii.2019.2897133,,A Blockchain-Based Nonrepudiation Network Computing Service Scheme for Industrial IoT,IEEE Transactions on Industrial Informatics,2019,article,applied_cryptography,4,85.0,138.0,56.3293254252152,230,55.2964,True,False,baseline,ablockchainbasednonrepudiationnetworkcomputingserviceschemeforindustrialiot,56.3293254252152,landmark,True507160693558,10.1007/s10473-026-0325-9,,Passivity-based synchronization of Markovian jump inertial neural networks via adaptive event-driven protocol and its application to image encryption,Acta Mathematica Scientia,2026,article,applied_cryptography,1,21.0,21.0,73.7245394876138,21,88.0004,False,False,post,passivitybasedsynchronizationofmarkovianjumpinertialneuralnetworksviaadaptiveeventdrivenprotocolanditsapplicationtoimageencryption,53.24487865600877,landmark,True514254706618,10.6028/nist.ir.8309,,Status report on the second round of the NIST post-quantum cryptography standardization process,,2020,report,applied_cryptography,2,68.0,152.0,52.898263027295286,296,,True,True,baseline,statusreportonthesecondroundofthenistpostquantumcryptographystandardizationprocess,52.898263027295286,landmark,True524248175462,10.2139/ssrn.3440802,,Bitcoin: A Peer-to-Peer Electronic Cash System,SSRN Electronic Journal,2019,preprint,blockchain,3,5543.0,7534.0,1699.155537022696,13922,,False,True,baseline,bitcoinapeertopeerelectroniccashsystem,1699.155537022696,landmark,True532961501274,10.1007/978-3-030-17740-9_3,,Aleksander Berentsen Recommends “Bitcoin: A Peer-to-Peer Electronic Cash System” by Satoshi Nakamoto,Springer eBooks,2019,book-chapter,blockchain,2,144.0,226.0,220.05819391042294,259,176.7032,False,False,baseline,aleksanderberentsenrecommendsbitcoinapeertopeerelectroniccashsystembysatoshinakamoto,220.05819391042294,landmark,True542898069736,10.1080/00207543.2018.1533261,,Blockchain technology and its relationships to sustainable supply chain management,International Journal of Production Research,2018,article,blockchain,2,100.0,389.0,206.24666837724715,4083,331.4245,True,False,baseline,blockchaintechnologyanditsrelationshipstosustainablesupplychainmanagement,206.24666837724715,landmark,True552905867785,10.1109/access.2019.2896108,1805.02707,A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks,IEEE Access,2019,article,blockchain,4,279.0,493.0,201.23447416399344,995,178.3857,True,True,baseline,asurveyonconsensusmechanismsandminingstrategymanagementinblockchainnetworks,201.23447416399344,landmark,True564320717855,10.1016/j.future.2023.02.008,,Blockchain for the metaverse: A Review,Future Generation Computer Systems,2023,article,blockchain,4,569.0,763.0,178.82871696911812,771,171.5415,True,True,baseline,blockchainforthemetaverseareview,178.82871696911812,landmark,True573162358200,10.48550/arxiv.2105.07447,2105.07447,"Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges",arXiv (Cornell University),2021,preprint,blockchain,3,157.0,387.0,157.1901226597805,577,,True,True,baseline,nonfungibletokennftoverviewevaluationopportunitiesandchallenges,157.1901226597805,landmark,True583123868249,10.1093/rfs/hhz015,,"Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed through Cryptocurrencies?",Review of Financial Studies,2019,article,blockchain,2,246.0,368.0,150.21153446724054,1051,145.8951,True,True,baseline,sexdrugsandbitcoinhowmuchillegalactivityisfinancedthroughcryptocurrencies,150.21153446724054,landmark,True592907683311,10.1109/access.2018.2890507,,Blockchain for AI: Review and Open Research Challenges,IEEE Access,2019,article,blockchain,1,200.0,342.0,139.59876301031593,997,153.0805,True,True,baseline,blockchainforaireviewandopenresearchchallenges,139.59876301031593,landmark,True604400662300,10.38124/ijisrt/ijisrt24jul180,,Collaborative Networks: Integrating Blockchain for Enhanced Trust and Transparency,International Journal of Innovative Science and Research Technology (IJISRT),2024,article,blockchain,1,449.0,449.0,135.18269519575335,449,171.8245,True,True,post,collaborativenetworksintegratingblockchainforenhancedtrustandtransparency,135.18269519575335,landmark,True614402275599,10.38124/ijisrt/ijisrt24aug1218,,Implementing Blockchain Technology for Optimized Supply Chain and Enhanced Sustainability,International Journal of Innovative Science and Research Technology (IJISRT),2024,article,blockchain,1,447.0,449.0,135.18269519575335,449,171.8245,True,True,post,implementingblockchaintechnologyforoptimizedsupplychainandenhancedsustainability,135.18269519575335,landmark,True622606010969,10.1145/3183367,1704.03610,Blockchains for Business Process Management - Challenges and Opportunities,ACM Transactions on Management Information Systems,2018,article,blockchain,3,145.0,253.0,134.13986400885224,704,145.0233,True,True,baseline,blockchainsforbusinessprocessmanagementchallengesandopportunities,134.13986400885224,landmark,True632914735843,10.1109/tsmc.2019.2895123,,"Blockchain-Enabled Smart Contracts: Architecture, Applications, and Future Trends",IEEE Transactions on Systems Man and Cybernetics Systems,2019,article,blockchain,2,136.0,314.0,128.1696245182433,1157,157.1419,True,False,baseline,blockchainenabledsmartcontractsarchitectureapplicationsandfuturetrends,128.1696245182433,landmark,True642973816953,10.7551/mitpress/11645.003.0010,,How Blockchain Will Change Organizations,The MIT Press eBooks,2018,book-chapter,blockchain,1,55.0,109.0,128.09387022313413,375,138.4143,True,False,baseline,howblockchainwillchangeorganizations,128.09387022313413,landmark,True653123722958,10.1002/isaf.1424,,Toward an ontology‐driven blockchain design for supply‐chain provenance,"Intelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management",2018,article,blockchain,2,122.0,233.0,123.53592218997065,651,132.5698,True,False,baseline,towardanontologydrivenblockchaindesignforsupplychainprovenance,123.53592218997065,landmark,True662800028583,10.1016/bs.adcom.2018.03.006,,Blockchain Technology Use Cases in Healthcare,Advances in computers,2018,book-chapter,blockchain,1,42.0,100.0,117.51731213131572,434,154.6983,False,False,baseline,blockchaintechnologyusecasesinhealthcare,117.51731213131572,landmark,True672784387076,10.1017/s1744137417000200,,Blockchains and the economic institutions of capitalism,Journal of Institutional Economics,2018,article,blockchain,4,143.0,220.0,116.64336000769761,577,110.4748,True,True,baseline,blockchainsandtheeconomicinstitutionsofcapitalism,116.64336000769761,landmark,True684380356557,10.4018/978-1-6684-5422-0.ch012,,Blockchain Technology in Renovating Healthcare,Advances in healthcare information systems and administration book series,2023,book-chapter,blockchain,2,139.0,169.0,116.33373815885354,171,116.723,True,False,post,blockchaintechnologyinrenovatinghealthcare,116.33373815885354,landmark,True692965761488,10.1016/j.tifs.2019.07.034,1908.07391,The rise of blockchain technology in agriculture and food supply chains,Trends in Food Science & Technology,2019,article,blockchain,2,102.0,285.0,116.33230250859661,1261,154.3302,True,True,baseline,theriseofblockchaintechnologyinagricultureandfoodsupplychains,116.33230250859661,landmark,True706906013109,10.15495/epub_ubt_00007183,,Blockchain Adoption in Organizations : Technical Barriers and Potential Solutions,EPub Bayreuth (University of Bayreuth),2023,dissertation,blockchain,5,3.0,4.0,115.45945945945945,5,,True,True,post,blockchainadoptioninorganizationstechnicalbarriersandpotentialsolutions,115.45945945945945,landmark,True714386476569,10.11606/d.55.2023.tde-06092023-102200,,Blockchain-based data governance for privacy-preserving in multi-stakeholder settings,,2023,dissertation,blockchain,3,3.0,4.0,115.45945945945945,4,,True,False,post,blockchainbaseddatagovernanceforprivacypreservinginmultistakeholdersettings,115.45945945945945,landmark,True722803836845,10.1016/j.joule.2018.04.016,,Bitcoin's Growing Energy Problem,Joule,2018,article,blockchain,1,119.0,217.0,115.05276873486537,687,112.4835,False,True,baseline,bitcoinsgrowingenergyproblem,115.05276873486537,landmark,True732927344969,10.2139/ssrn.6433340,,Global Blockchain Benchmarking Study,SSRN Electronic Journal,2026,preprint,blockchain,1,45.0,45.0,1480.9248554913295,45,,False,True,post,globalblockchainbenchmarkingstudy,114.09616854859023,landmark,True742909202584,10.1109/tii.2019.2893433,,Privacy-Preserving Energy Trading Using Consortium Blockchain in Smart Grid,IEEE Transactions on Industrial Informatics,2019,article,blockchain,3,163.0,274.0,111.84228381528236,573,115.279,True,False,baseline,privacypreservingenergytradingusingconsortiumblockchaininsmartgrid,111.84228381528236,landmark,True753163490558,10.15760/etd.7527,,Assessment of the Blockchain Technology Adoption for the Management of the Electronic Health Record Systems,,2021,dissertation,blockchain,3,4.0,10.0,110.32663316582915,20,,True,True,baseline,assessmentoftheblockchaintechnologyadoptionforthemanagementoftheelectronichealthrecordsystems,110.32663316582915,landmark,True762783535300,10.3390/logistics2010002,,The Supply Chain Has No Clothes: Technology Adoption of Blockchain for Supply Chain Transparency,Logistics,2018,article,blockchain,2,82.0,208.0,110.28099491636864,951,138.9974,True,True,baseline,thesupplychainhasnoclothestechnologyadoptionofblockchainforsupplychaintransparency,110.28099491636864,landmark,True774408302972,10.3390/educsci15030343,,The Impact of Artificial Intelligence (AI) on Students’ Academic Development,Education Sciences,2025,article,privacy_preserving_computation,1,469.0,469.0,407.18625178855723,469,511.2876,True,True,post,theimpactofartificialintelligenceaionstudentsacademicdevelopment,204.93966523196156,landmark,True787127055292,10.3389/feduc.2025.1648661,,Artificial intelligence in higher education: a systematic review of its impact on student engagement and the mediating role of teaching methods,Frontiers in Education,2026,review,privacy_preserving_computation,1,54.0,54.0,102.8810408921933,54,112.4196,True,True,post,artificialintelligenceinhighereducationasystematicreviewofitsimpactonstudentengagementandthemediatingroleofteachingmethods,136.9154022583083,landmark,True793036422000,10.1109/access.2020.3010226,2006.10306,A Survey of COVID-19 Contact Tracing Apps,IEEE Access,2020,article,privacy_preserving_computation,4,302.0,443.0,128.99802950839438,582,117.8531,True,True,baseline,asurveyofcovid19contacttracingapps,128.99802950839438,landmark,True804302383326,,,Private Information Retrieval with Side Information,OakTrust (Texas A&M University Libraries),2018,dissertation,privacy_preserving_computation,5,24.0,28.0,124.06412825651303,28,,True,True,baseline,privateinformationretrievalwithsideinformation,124.06412825651303,landmark,True813014084098,10.48550/arxiv.2003.11511,2003.11511,Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs,arXiv (Cornell University),2020,preprint,privacy_preserving_computation,2,247.0,307.0,112.68777120315582,350,,True,True,baseline,contacttracingmobileappsforcovid19privacyconsiderationsandrelatedtradeoffs,112.68777120315582,landmark,True823013489453,10.1038/s41591-020-0832-5,,On the responsible use of digital data to tackle the COVID-19 pandemic,Nature Medicine,2020,article,privacy_preserving_computation,1,284.0,368.0,107.15863399342919,488,96.9324,False,True,baseline,ontheresponsibleuseofdigitaldatatotacklethecovid19pandemic,107.15863399342919,landmark,True833021970994,10.2196/19857,,Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study,JMIR mhealth and uhealth,2020,article,privacy_preserving_computation,2,171.0,290.0,84.4456626578654,402,81.5906,True,True,baseline,acceptabilityofappbasedcontacttracingforcovid19crosscountrysurveystudy,84.4456626578654,landmark,True847137155252,10.1145/3801079,,Differentially Private Federated Learning: A Systematic Review,ACM Computing Surveys,2026,review,privacy_preserving_computation,4,33.0,33.0,37.86734693877551,21,27.926,True,True,post,differentiallyprivatefederatedlearningasystematicreview,83.6705236022995,landmark,True854407111324,10.3389/fpsyg.2025.1498132,,Exploring the effects of artificial intelligence on student and academic well-being in higher education: a mini-review,Frontiers in Psychology,2025,review,privacy_preserving_computation,1,190.0,190.0,29.307559598494354,190,32.5824,True,True,post,exploringtheeffectsofartificialintelligenceonstudentandacademicwellbeinginhighereducationaminireview,83.02459785516567,landmark,True864406636601,10.1016/j.accinf.2025.100734,,Challenges and opportunities for artificial intelligence in auditing: Evidence from the field,International Journal of Accounting Information Systems,2025,article,privacy_preserving_computation,2,183.0,183.0,31.465784036766877,183,92.2647,True,True,post,challengesandopportunitiesforartificialintelligenceinauditingevidencefromthefield,79.9657968815543,landmark,True874286110439,10.1515/9783839459249-009,,»Ohne die geht’s nicht« Zur Rolle von Apple und Google bei der Corona-Warn-App,transcript Verlag eBooks,2022,book-chapter,privacy_preserving_computation,1,100.0,100.0,77.6986788965706,100,67.9524,True,True,baseline,ohnediegehtsnichtzurrollevonappleundgooglebeidercoronawarnapp,77.6986788965706,landmark,True884399027046,10.22024/unikent/01.02.102717,,Secure and Privacy-preserving Federated Learning with Explainable Artificial Intelligence for Smart Healthcare System,theses.fr (ABES),2023,dissertation,privacy_preserving_computation,5,8.0,9.0,74.86447638603696,9,,True,True,post,secureandprivacypreservingfederatedlearningwithexplainableartificialintelligenceforsmarthealthcaresystem,74.86447638603696,landmark,True893018102029,10.1609/aaai.v36i8.20825,2004.12088,SplitFed: When Federated Learning Meets Split Learning,Proceedings of the AAAI Conference on Artificial Intelligence,2022,conference-paper,privacy_preserving_computation,7,202.0,373.0,74.10667093485475,720,85.2216,True,True,baseline,splitfedwhenfederatedlearningmeetssplitlearning,74.10667093485475,landmark,True904414891672,10.1038/s41746-025-02280-z,2506.00660,"Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications",npj Digital Medicine,2026,article,privacy_preserving_computation,5,29.0,29.0,32.27124442472503,29,104.7659,True,True,post,differentialprivacyformedicaldeeplearningmethodstradeoffsanddeploymentimplications,73.52864195353592,landmark,True914205766150,10.1787/8f394636-en,,Tracking and tracing COVID: Protecting privacy and data while using apps and biometrics,OECD policy responses to coronavirus (Covid-19),2020,report,privacy_preserving_computation,2,124.0,130.0,72.61058109280138,139,,False,False,baseline,trackingandtracingcovidprotectingprivacyanddatawhileusingappsandbiometrics,72.61058109280138,landmark,True924412565294,10.1145/3749987,2209.02299,A Survey of Machine Unlearning,ACM Transactions on Intelligent Systems and Technology,2025,article,privacy_preserving_computation,7,164.0,164.0,55.86648716521469,164,47.4381,True,True,post,asurveyofmachineunlearning,71.66333709603774,landmark,True932797770018,10.1016/j.future.2018.02.050,,A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment,Future Generation Computer Systems,2018,article,privacy_preserving_computation,3,55.0,132.0,69.98601600461856,192,60.259,False,False,baseline,atwostagelocalitysensitivehashingbasedapproachforprivacypreservingmobileservicerecommendationincrossplatformedgeenvironment,69.98601600461856,landmark,True947135198685,10.1098/rsos.250959,,Breaking interprovincial data silos: how federated learning can unlock Canada's public health potential,Royal Society Open Science,2026,article,privacy_preserving_computation,3,27.0,27.0,30.045641360950892,27,46.964,True,True,post,breakinginterprovincialdatasiloshowfederatedlearningcanunlockcanadaspublichealthpotential,68.45770112915415,landmark,True952799796316,10.1155/2018/3075849,,Privacy‐Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment,Wireless Communications and Mobile Computing,2018,article,privacy_preserving_computation,5,58.0,129.0,68.39542473178632,166,58.2504,True,True,baseline,privacyawaremultidimensionalmobileservicequalitypredictionandrecommendationindistributedfogenvironment,68.39542473178632,landmark,True963016632787,10.1109/tifs.2020.2988575,1911.00222,Federated Learning With Differential Privacy: Algorithms and Performance Analysis,IEEE Transactions on Information Forensics and Security,2020,article,privacy_preserving_computation,8,206.0,490.0,61.28715445275716,2379,83.9132,True,True,baseline,federatedlearningwithdifferentialprivacyalgorithmsandperformanceanalysis,61.28715445275716,landmark,True977155564432,10.1109/isbdas69350.2026.11484374,,Privacy-Preserving Anomaly Detection in Cloud Services using Hierarchical Federated Learning with Differential Privacy,,2026,conference-paper,privacy_preserving_computation,6,24.0,24.0,184.20139553974553,24,221.8777,True,True,post,privacypreservinganomalydetectionincloudservicesusinghierarchicalfederatedlearningwithdifferentialprivacy,60.851289892581455,landmark,True984389365236,10.3390/educsci13121216,,Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review,Education Sciences,2023,article,privacy_preserving_computation,1,200.0,642.0,58.93867484628822,957,83.985,True,True,post,adaptivelearningusingartificialintelligenceinelearningaliteraturereview,58.93867484628822,landmark,True993041977451,,,Analyzing adoption of contact tracing apps using UTAUT,,2020,dissertation,privacy_preserving_computation,1,1.0,6.0,56.4292343387471,7,,True,False,baseline,analyzingadoptionofcontacttracingappsusingutaut,56.4292343387471,landmark,True1007154488736,10.1109/iccsc67078.2026.11468534,,Predicting Mental Health Challenges Among University Students Using a Federated CNN-BiLSTM-Attention Framework,"International Conference on Computing, Sciences and Communications (ICCSC)",2026,conference-paper,privacy_preserving_computation,3,22.0,22.0,289.37333333333333,22,365.288,True,False,post,predictingmentalhealthchallengesamonguniversitystudentsusingafederatedcnnbilstmattentionframework,55.78034906819967,landmark,True1012790400697,10.1109/access.2018.2806881,,"A Survey on Big Data Market: Pricing, Trading and Protection",IEEE Access,2018,article,privacy_preserving_computation,5,60.0,105.0,55.670694549128406,356,61.4642,True,True,baseline,asurveyonbigdatamarketpricingtradingandprotection,55.670694549128406,landmark,True1027125492275,10.1016/j.ress.2026.112271,,Source-free domain adaptation for cross-domain remaining useful life prediction: A distributed federated learning perspective,Reliability Engineering & System Safety,2026,article,federated_learning,1,34.0,34.0,37.662406639004146,34,94.5159,False,False,post,sourcefreedomainadaptationforcrossdomainremainingusefullifepredictionadistributedfederatedlearningperspective,86.2059940144904,landmark,True1034382463479,10.1609/aaai.v37i9.26330,2212.01197,FedALA: Adaptive Local Aggregation for Personalized Federated Learning,Proceedings of the AAAI Conference on Artificial Intelligence,2023,conference-paper,federated_learning,9,131.0,320.0,73.79786537022606,420,79.3941,True,True,post,fedalaadaptivelocalaggregationforpersonalizedfederatedlearning,73.79786537022606,landmark,True1044220990555,10.14711/thesis-991012994506603412,,Robust federated learning with attack-adaptive aggregation,,2021,dissertation,federated_learning,5,9.0,14.0,63.67692307692308,24,,True,False,baseline,robustfederatedlearningwithattackadaptiveaggregation,63.67692307692308,landmark,True1053133814152,10.1109/tnnls.2022.3160699,2103.00710,Towards Personalized Federated Learning,IEEE Transactions on Neural Networks and Learning Systems,2022,article,federated_learning,7,231.0,554.0,57.083945154517146,1182,74.5614,True,True,baseline,towardspersonalizedfederatedlearning,57.083945154517146,landmark,True1063159080474,,,Federated Learning for Internet of Things: A Comprehensive Survey,Deakin Research Online (Deakin University),2021,article,federated_learning,4,142.0,416.0,48.48164672055449,894,61.8395,True,True,baseline,federatedlearningforinternetofthingsacomprehensivesurvey,48.48164672055449,landmark,True1073100779497,10.1007/s41666-020-00082-4,1911.06270,Federated Learning for Healthcare Informatics,Journal of Healthcare Informatics Research,2020,article,federated_learning,2,140.0,343.0,42.90100811693001,1515,58.5309,False,True,baseline,federatedlearningforhealthcareinformatics,42.90100811693001,landmark,True1082995735216,,,Fair Resource Allocation in Federated Learning,International Conference on Learning Representations,2020,conference-paper,federated_learning,4,225.0,300.0,42.51886520143499,120,11.8933,True,False,baseline,fairresourceallocationinfederatedlearning,42.51886520143499,landmark,True1097159977201,10.1109/tmc.2026.3688489,,AsyncFed-SAR: Toward Communication-Efficient Asynchronous Federated Learning for SAR Image Target Recognition,IEEE Transactions on Mobile Computing,2026,article,federated_learning,3,16.0,16.0,17.80482451019312,16,57.8019,False,False,post,asyncfedsartowardcommunicationefficientasynchronousfederatedlearningforsarimagetargetrecognition,40.567526595054304,landmark,True1103045674654,10.1038/s41598-020-69250-1,,Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data,Scientific Reports,2020,article,federated_learning,4,122.0,323.0,40.39949160865421,1607,50.8594,True,True,baseline,federatedlearninginmedicinefacilitatingmultiinstitutionalcollaborationswithoutsharingpatientdata,40.39949160865421,landmark,True1113018464563,10.1109/mis.2020.2988604,1907.09173,FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare,IEEE Intelligent Systems,2020,article,federated_learning,7,141.0,309.0,38.648430052861144,999,49.6281,True,True,baseline,fedhealthafederatedtransferlearningframeworkforwearablehealthcare,38.648430052861144,landmark,True1124387105517,10.1145/3625558,2307.10616,Heterogeneous Federated Learning: State-of-the-art and Research Challenges,ACM Computing Surveys,2023,article,federated_learning,7,133.0,417.0,38.282597213243285,608,53.4131,True,True,post,heterogeneousfederatedlearningstateoftheartandresearchchallenges,38.282597213243285,landmark,True1134408476406,10.1016/j.jnca.2025.104165,,CBRFL: A framework for Committee-based Byzantine-Resilient Federated Learning,Journal of Network and Computer Applications,2025,article,federated_learning,4,83.0,83.0,28.27389289459036,83,34.5383,False,False,post,cbrflaframeworkforcommitteebasedbyzantineresilientfederatedlearning,36.268640115677634,landmark,True1144407318675,10.1016/j.aei.2025.103179,,Federated learning-empowered smart manufacturing and product lifecycle management: A review,Advanced Engineering Informatics,2025,article,federated_learning,2,82.0,82.0,27.933243582607346,82,34.1222,False,False,post,federatedlearningempoweredsmartmanufacturingandproductlifecyclemanagementareview,35.83166854801887,landmark,True1154393156671,10.1609/aaai.v38i15.29617,2401.03230,FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning,Proceedings of the AAAI Conference on Artificial Intelligence,2024,conference-paper,federated_learning,8,66.0,121.0,33.633393493997694,121,36.8098,True,True,post,fedtgptrainableglobalprototypeswithadaptivemarginenhancedcontrastivelearningfordataandmodelheterogeneityinfederatedlearning,33.633393493997694,landmark,True1167118779462,10.1016/j.compeleceng.2025.110924,,Federated learning in healthcare: Recent progress and challenges,Computers & Electrical Engineering,2026,article,federated_learning,2,13.0,13.0,14.466419914531912,13,46.964,False,False,post,federatedlearninginhealthcarerecentprogressandchallenges,32.96111535848162,landmark,True1173196371845,10.1016/j.neucom.2021.07.098,2106.06843,Federated learning on non-IID data: A survey,Neurocomputing,2021,article,federated_learning,6,75.0,277.0,32.28225034036922,1043,48.3425,False,True,baseline,federatedlearningonnoniiddataasurvey,32.28225034036922,landmark,True1187133255133,10.3390/computers15030155,,"Federated Learning: A Survey of Core Challenges, Current Methods, and Opportunities",Computers,2026,article,federated_learning,5,12.0,12.0,13.353618382644841,12,43.3514,True,True,post,federatedlearningasurveyofcorechallengescurrentmethodsandopportunities,30.425644946290728,landmark,True1197104465905,10.71781/10873,,Re-weighted softmax cross-entropy to control forgetting in federated learning,Papyrus : Institutional Repository (Université de Montréal),2022,dissertation,federated_learning,3,1.0,4.0,29.956140350877195,0,,True,True,post,reweightedsoftmaxcrossentropytocontrolforgettinginfederatedlearning,29.956140350877195,landmark,True1204406269727,10.1016/j.eswa.2024.126354,,Federated learning framework based on trimmed mean aggregation rules,Expert Systems with Applications,2025,article,federated_learning,3,67.0,67.0,22.8235039028621,67,22.0546,False,True,post,federatedlearningframeworkbasedontrimmedmeanaggregationrules,29.277095033137364,landmark,True1214402716118,10.1109/cvpr52733.2024.01139,,FedAS: Bridging Inconsistency in Personalized Federated Learning,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2024,conference-paper,federated_learning,5,56.0,104.0,28.90804068905587,104,31.6381,True,False,post,fedasbridginginconsistencyinpersonalizedfederatedlearning,28.90804068905587,landmark,True1224412162472,10.1038/s41746-025-01836-3,,A scoping review of the governance of federated learning in healthcare,npj Digital Medicine,2025,article,federated_learning,2,65.0,65.0,22.142205278896068,65,27.048,True,True,post,ascopingreviewofthegovernanceoffederatedlearninginhealthcare,28.403151897819832,landmark,True1234293155325,10.1016/j.ipm.2022.103061,,"Federated learning review: Fundamentals, enabling technologies, and future applications",Information Processing & Management,2022,article,federated_learning,3,116.0,274.0,28.232853740681765,550,37.8866,False,False,baseline,federatedlearningreviewfundamentalsenablingtechnologiesandfutureapplications,28.232853740681765,landmark,True1247121318593,10.1007/s10115-025-02673-2,,A comprehensive survey on federated learning for privacy preservation in digital healthcare applications,Knowledge and Information Systems,2026,article,federated_learning,3,11.0,11.0,12.24081685075777,11,39.7388,False,False,post,acomprehensivesurveyonfederatedlearningforprivacypreservationindigitalhealthcareapplications,27.890174534099835,landmark,True1254415878254,10.1109/jiot.2026.3669440,2510.15300,DFCA: Decentralized Federated Clustering Algorithm,IEEE Internet of Things Journal,2026,article,federated_learning,6,10.0,10.0,11.1280153188707,10,36.1262,True,True,post,dfcadecentralizedfederatedclusteringalgorithm,25.35470412190894,landmark,True1264312634912,10.1109/cvpr52688.2022.00985,2205.03993,Layer-wised Model Aggregation for Personalized Federated Learning,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2022,conference-paper,federated_learning,6,48.0,126.0,25.033352648235116,240,30.554,True,True,baseline,layerwisedmodelaggregationforpersonalizedfederatedlearning,25.033352648235116,landmark,True127