Computer Science ›› 2026, Vol. 53 ›› Issue (10): 423-439.doi: 10.11896/jsjkx.250900163

• Information Security • Previous Articles     Next Articles

Survey on Differential Privacy Synthesis of Structured Data

FANG Xianjin, LUO Chenyue   

  1. School of Computer Science and Engineering,Anhui University of Science and Technology,Huainan,Anhui 232001, China
  • Received:2025-09-28 Revised:2025-12-31 Published:2026-10-13
  • About author:FANG Xianjin,born in 1970,Ph.D,professor,Ph.D supervisor,is a distinguished member of CCF(No.38710D).His main research interests include network and information security,and intelligent computing.
    LUO Chenyue,born in 2001,postgra-duate,is a student member of CCF(No.X8948G).His main research interests include data security and privacy protection.
  • Supported by:
    General Program of the National Natural Science Foundations of China(61572034).

Abstract: Data sharing is essential for artificial intelligence and large-scale data analytics,but it is often constrained by privacy and security concerns.Privacy-preserving synthetic data has emerged as a promising solution,with differential privacy(DP) at the forefront due to its rigorous mathematical guarantees.DP-based synthesis generates datasets that closely approximate the original data distribution while providing formal privacy assurances,balancing utility and security.The multi-source and heterogeneous nature of real-world big data has led to numerous synthesis methods,yet a general-purpose approach is still lacking.To address these issues,this paper systematically reviews DP-based synthesis techniques for structured data,including tabular,trajectory,and graph data,and proposes a unified framework for differentially private structured data synthesis.It introduces multidimensional metrics for evaluating privacy and utility,identifies key challenges,and highlights potential research directions.In addition,considering emerging AI safety concerns and the rapid rise of artificial intelligence generated content(AIGC),it discusses the application prospects of DP-based synthetic data in large language model training,federated learning,and related domains.

Key words: Differential privacy, Privacy-preserving, Data security, Data synthesis, Privacy computing

CLC Number: 

  • TP309.2
[1] VOIGT P,VON DEM BUSSCHE A.The EU General Data Protection Regulation(GDPR):A Practical Guide[M].Cham:Springer,2017.
[2] DWORK C.Differential Privacy[C]//Automata,Languages and Programming.Berlin:Springer,2006:1-12.
[3] JORDON J,SZPRUCH L,HOUSSIAU F,et al.Synthetic Data:What,Why and How?[J].arXiv:2205.03257,2022.
[4] LIU F,CHENG Z,CHEN H,et al.Privacy-Preserving Synthetic Data Generation for Recommendation Systems[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2022:1379-1389.
[5] SU B,WANG Y,SCHIAVAZZI D,et al.Privacy-Preserving Data Synthesis via Differentially Private Normalizing Flows with Application to Electronic Health Records Data[C]//Proceedings of the AAAI Symposium Series.2023:161-167.
[6] YANG M,CHI C H,LAM K Y,et al.Tabular Data Synthesis with Differential Privacy:A Survey[J].arXiv:2411.03351,2024.
[7] MIRANDA-PASCUAL À,GUERRA-BALBOA P,PARRA-ARNAU J,et al.SoK:Differentially Private Publication of Trajectory Data[J].Proceedings on Privacy Enhancing Technologies,2023,2023:496-516.
[8] JIANG H,PEI J,YU D,et al.Applications of Differential Privacy in Social Network Analysis:A Survey[J].IEEE Transactions on Knowledge and Data Engineering,2023,35(1):108-127.
[9] SWEENEY L.k-anonymity:A Model for Protecting Privacy[J].International Journal of Uncertainty,Fuzziness and Know-ledge-Based Systems,2002,10(5):557-570.
[10] MACHANAVAJJHALA A,GEHRKE J,KIFER D,et al.l-Diversity:Privacy Beyond k-Anonymity[C]//Proceedings of the 22nd International Conference on Data Engineering.2006.
[11] LI N,LI T,VENKATASUBRAMANIAN S.t-Closeness:Privacy Beyond k-Anonymity and l-Diversity[C]//Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering.2007.
[12] RIVEST R L,DERTOUZOS M L.On Data Banks and Privacy Homomorphisms[R].Cambridge:MIT Laboratory for Compu-ter Science,1978.
[13] HENECKAW,KÖGL S K,SADEGHI A R,et al.TASTY:Tool for Automating Secure Two-Party Computations[C]//Procee-dings of the 17th ACM Conference on Computer and Communications Security.ACM,2010:451-462.
[14] CORMODE G,JHA S,KULKARNI T,et al.Privacy at Scale:Local Differential Privacy in Practice[C]//Proceedings of the 2018 International Conference on Management of Data.ACM,2018:1655-1658.
[15] ZHU T,LI G,ZHOU W,et al.Differentially Private Data Publishing and Analysis:A Survey[J].IEEE Transactions on Knowledge and Data Engineering,2017,29(8):1619-1638.
[16] DWORK C,MCSHERRY F,NISSIM K,et al.Calibrating Noise to Sensitivity in Private Data Analysis[C]//Proceedings of the 3rd Conference on Theory of Cryptography.Springer,2006:265-284.
[17] MCSHERRY F,TALWAR K.Mechanism Design via Differential Privacy[C]//Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science.IEEE,2007:94-103.
[18] DWORK C,KENTHAPADI K,MCSHERRY F,et al.Our Data,Ourselves:Privacy Via Distributed Noise Generation[C]//Advances in Cryptology-EUROCRYPT 2006.Berlin:Springer,2006.
[19] YE Q Q,MENG X F,ZHU M J,et al.Survey on Local Differential Privacy[J].Journal of Software,2018,29(7):1981-2005.
[20] DWORK C,ROTHBLUM G N.Concentrated Differential Privacy[J].arXiv:1603.01887,2016.
[21] KONG X,LI M,MA K,et al.Big Trajectory Data:A Survey of Applications andServices[J].IEEE Access,2018,6:58295-58306.
[22] STROGATZ S H.Exploring Complex Networks[J].Nature,2001,410(6825):268-276.
[23] HU Y,WU F,LI Q,et al.SoK:Privacy-Preserving Data Synthesis[C]//Proceedings of the 2024 IEEE Symposium on Security and Privacy.2024.
[24] SUCAR L E.Probabilistic Graphical Models[M].Cham:Sprin-ger,2021.
[25] STEPHENSON T.An Introduction to Bayesian Network Theory and Usage[R].Lausanne:IDIAP Research Institute,2000.
[26] ZHANG J,CORMODE G,PROCOPIUC C M,et al.PrivBayes:Private Data Release via Bayesian Networks[J].ACM Transactions on Database Systems,2017,42(4):Article 25.
[27] BAO E,XIAO X,ZHAO J,et al.Synthetic Data Generation with Differential Privacy via Bayesian Networks[J].Journal of Privacy and Confidentiality,2021,11(3).
[28] METZLER D,CROFT W B.A Markov Random Field Model for Term Dependencies[C]//Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.Salvador:Association for Computing Machinery,2005:472-479.
[29] MCKENNA R,MIKLAU G,SHELDON D.Winning the NIST Contest:A Scalable and General Approach to Differentially Private Synthetic Data[J].Journal of Privacy and Confidentiality,2021,11(3).
[30] CAI K,LEI X,WEI J,et al.Data Synthesis via DifferentiallyPrivate Markov Random Fields[J].Proceedings of the VLDB Endowment,2021,14(11):2190-2202.
[31] BARBER D.The Junction Tree Algorithm[M]//Bayesian Reasoning and Machine Learning.Cambridge:Cambridge University Press,2012:102-126.
[32] JIA J,LI X,LI T,et al.ABSyn:An Accurate Differentially Private Data Synthesis Scheme With Adaptive Selection and Batch Processes[J].IEEE Transactions on Information Forensics and Security,2024,19:8338-8352.
[33] ZHANG Z,WANG T,LI N,et al.PrivSyn:Differentially Private Data Synthesis[C]//Proceedings of the 30th USENIX Security Symposium.USENIX Association,2021:929-946.
[34] CAI K,XIAO X,CORMODE G.PrivLava:Synthesizing Rela-tional Data with Foreign Keys under Differential Privacy[J]Proceedings of the ACM on Management of Data,2023,1(2):Article 142.
[35] ZHU Y W,WANG K,ZHOU Y Q.A Multi-party Vertically Partitioned Data Synthesis Mechanism with Personalized Diffe-rential Privacy[J].Journal of Electronics & Information Techno-logy,2024,46(5):2159-2176.
[36] SKLAR M J.Fonctions de Répartition à n Dimensions et Leurs Marges[J].Publications de l'Institut de Statistique de l'Université de Paris,1959,8:229-231.
[37] BOUYÉ E,DURRLEMAN V,NIKEGHBALI A,et al.Copulas for Finance:A Reading Guide and Some Applications[R].Paris:Groupe de Recherche Opérationnelle,Crédit Lyonnais,2000.
[38] BEDFORD T,COOKE R M.Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines[J].Annals of Mathematics and Artificial Intelligence,2001,32(1):245-268.
[39] LI H,XIONG L,JIANG X.Differentially Private Synthesizationof Multi-Dimensional Data Using Copula Functions[C]//Proceedings of the International Conference on Extending Database Technology.2014:475-486.
[40] ASGHAR H J,DING M,RAKOTOARIVELO T,et al.Diffe-rentially Private Release of Datasets Using Gaussian Copula[J].Journal of Privacy and Confidentiality,2021,10(2).
[41] OSORIO-MARULANDA P A,RAMIREZ J E C,JIMÉNEZ M H,et al.Differentially Private Non Parametric Copulas:Generating Synthetic Data with Non Parametric Copulas under Privacy Guarantees[J].arXiv:2409.18611,2024.
[42] DISSMANN J,BRECHMANN E C,CZADO C,et al.Selecting and Estimating Regular Vine Copulae and Application to Financial Returns[J].Computational Statistics & Data Analysis,2013,59:52-69.
[43] GAMBS S,LADOUCEUR F,LAURENT A,et al.GrowingSynthetic Data through Differentially-Private Vine Copulas[J].Proceedings on Privacy Enhancing Technologies,2021,2021:122-141.
[44] GRIESBAUER E,CZADO C,FRIGESSI A,et al.TVineSynth:A Truncated C-Vine Copula Generator of Synthetic Tabular Data to Balance Privacy and Utility[J].arXiv:2503.15972,2025.
[45] KENTHAPADI K,KOROLOVA A,MIRONOV I,et al.Privacy via the Johnson-Lindenstrauss Transform[J].Journal of Privacy and Confidentiality,2013,5(1).
[46] JOHNSON W,LINDENSTRAUSS J.Extensions of LipschitzMaps into a Hilbert Space[J].Contemporary Mathematics,1984,26:189-206.
[47] XU C,REN J,ZHANG Y,et al.DPPro:Differentially Private High-Dimensional Data Release via Random Projection[J].IEEE Transactions on Information Forensics and Security,2017,12(12):3081-3093.
[48] CHANYASWAD T,LIU C,MITTAL P.RON-Gauss:Enhan-cing Utility in Non-Interactive Private Data Release[J].Proceedings on Privacy Enhancing Technologies,2019,2019:26-46.
[49] MECKES E.Projections of Probability Distributions:A Mea-sure-Theoretic Dvoretzky Theorem[M]//Geometric Aspects of Functional Analysis:Israel Seminar 2006-2010.Berlin:Sprin-ger,2012:317-326.
[50] HARDT M,LIGETT K,MCSHERRY F.A Simple and Practical Algorithm for Differentially Private Data Release[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems.Curran Associates Inc.,2012:2339-2347.
[51] HARDT M,ROTHBLUM G N.A Multiplicative WeightsMechanism for Privacy-Preserving Data Analysis[C]//Procee-dings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.2010.
[52] GABOARDI M,ARIAS E J G,HSU J,et al.Dual Query:Practical Private Query Release for High Dimensional Data[C]//Proceedings of the 31st International Conference on Machine Lear-ning.PMLR,2014:1170-1178.
[53] MCKENNA R,MULLINS B,SHELDON D,et al.AIM:An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data[J].Proceedings of the VLDB Endowment,2022,15(11):2599-2612.
[54] CHEN R,FUNG B C M,DESAI B C,et al.Differentially Private Transit Data Publication:A Case Study on the Montreal Transportation System[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2012:213-221.
[55] CHEN R,ACS G,CASTELLUCCIA C.Differentially PrivateSequential Data Publication via Variable-Length n-Grams[C]//Proceedings of the 2012 ACM Conference on Computer and Communications Security.ACM,2012:638-649.
[56] HE X,CORMODE G,MACHANAVAJJHALA A,et al.DPT:Differentially Private Trajectory Synthesis Using Hierarchical Reference Systems[J].Proceedings of the VLDB Endowment,2015,8(11):1154-1165.
[57] ZHANG J,XIAO X,XIE X.PrivTree:A Differentially Private Algorithm for Hierarchical Decompositions[C]//Proceedings of the 2016 International Conference on Management of Data.ACM,2016:155-170.
[58] CAI S,LYU X,LI X,et al.A Trajectory Released Scheme for the Internet of Vehicles Based on Differential Privacy[J].IEEE Transactions on Intelligent Transportation Systems,2022,23(9):16534-16547.
[59] ROY H,KANTARCIOGLU M,SWEENEY L.Practical Diffe-rentially Private Modeling of Human Movement Data[C]//Data and Applications Security and Privacy.Cham:Springer,2016.
[60] GURSOY M E,LIU L,TRUEX S,et al.Differentially Private and Utility Preserving Publication of Trajectory Data[J].IEEE Transactions on Mobile Computing,2019,18(10):2315-2329.
[61] GURSOY M E,LIU L,TRUEX S,et al.Utility-Aware Synthesis of Differentially Private and Attack-Resilient Location Traces[C]//Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security.ACM,2018:196-211.
[62] GURSOY M E,RAJASEKAR V,LIU L.Utility-OptimizedSynthesis of Differentially Private Location Traces[C]//Proceedings of the 2020 Second IEEE International Conference on Trust,Privacy and Security in Intelligent Systems and Applications.2020.
[63] WANG H,ZHANG Z,WANG T,et al.PrivTrace:Differentially Private Trajectory Synthesis by Adaptive Markov Models[C]//Proceedings of the 32nd USENIX Conference on Security Symposium.USENIX Association,2023.
[64] NIU Y,ZHANG J,TANG Z,et al.Privacy-Preserving Spatio-temporal Trajectory Generalization Publishing Scheme with Differential Privacy[J].Computers & Security,2025,156:104514.
[65] JIN Y G,YE F W,WU Q H.Location Privacy Protection Mechanisms for UAVs with Remote ID[J].Acta Aeronautica et Astronautica Sinica,2025,46(11):531341.
[66] LESKOVEC J,FALOUTSOS C.Scalable Modeling of RealGraphs Using Kronecker Multiplication[C]//Proceedings of the 24th International Conference on Machine Learning.ACM,2007:497-504.
[67] MAHADEVAN P,KRIOUKOV D,FALL K,et al.Systematic Topology Analysis and Generation Using Degree Correlations[J].SIGCOMM Computer Communication Review,2006,36(4):135-146.
[68] SNIJDERS T A B,PATTISON P E,ROBINS G L,et al.New Specifications for Exponential Random Graph Models[J].Sociological Methodology,2006,36(1):99-153.
[69] CLAUSET A,MOORE C,NEWMAN M E J.HierarchicalStructure and the Prediction of Missing Links in Networks[J].Nature,2008,453(7191):98-101.
[70] PFEIFFER J J,MORENO S,FOND T L,et al.AttributedGraph Models:Modeling Network Structure with Correlated Attributes[C]//Proceedings of the 23rd International Confe-rence on World Wide Web.ACM,2014:831-842.
[71] JORGENSEN Z,YU T,CORMODE G.Publishing Attributed Social Graphs with Formal Privacy Guarantees[C]//Procee-dings ofthe 2016 International Conference on Management of Data.ACM,2016:107-122.
[72] CHEN X,MAUW S,RAMÍREZ-CRUZ Y.Publishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee[J].Proceedings on Privacy Enhancing Techno-logies,2020,2020:131-152.
[73] HUANG H,ZHANG D,XIAO F,et al.Privacy-Preserving Approach PBCN in Social Network with Differential Privacy[J].IEEE Transactions on Network and Service Management,2020,17(2):931-945.
[74] YUAN Q,ZHANG Z,DU L,et al.PrivGraph:Differentially Private Graph Data Publication by Exploiting Community Information[C]//Proceedings of the 32nd USENIX Conference on Security Symposium.USENIX Association,2023.
[75] SHEN J,TIAN J,WANG Z,et al.Preserving Social Relationship Privacy via the Exponential Mechanism of Personalized Differential Privacy[J].IEEE Transactions on Computational Social Systems,2025,12(3):1164-1180.
[76] CHEN J,HU C,SHENG W,et al.Sensitivity-Aware Persona-lized Differential Privacy Guarantees for Online Social Networks[J].IEEE Transactions on Information Forensics and Security,2025,20:3116-3130.
[77] WANG S,ZHENG Y,JIA X,et al.PrivAGM: Secure Construction of Differentially Private Directed Attributed Graph Models on Decentralized Social Graphs[J].Proceedings of the VLDB Endowment,2025,18(11):4682-4694.
[78] AIELLO W,CHUNG F,LU L.A Random Graph Model for Massive Graphs[C]//Proceedings of the Thirty-Second Annual ACM Symposium on Theory of Computing.Association for Computing Machinery,2000:171-180.
[79] YUAN Q,ZHANG Z,DU L,et al.PSGraph:Differentially Private Streaming Graph Synthesis by Considering Temporal Dynamics[J].arXiv:2412.11369,2024.
[80] XU W,LI Z,LIU H,et al.Differentially Private WeightedGraphs Publication Under Continuous Monitoring[J].IEEE Transactions on Mobile Computing,2025,24(5):3735-3749.
[81] ABADI M,CHU A,GOODFELLOW I,et al.Deep Learningwith Differential Privacy[C]//Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security.ACM,2016:308-318.
[82] FLETCHER S,ROEGIEST A,HUDEK A K.Towards Protecting Sensitive Text with Differential Privacy[C]//Proceedings of the 2021 IEEE 20th International Conference on Trust,Security and Privacy in Computing and Communications.2021.
[83] YUE X,DU M,WANG T,et al.Differential Privacy for Text Analytics via Natural Text Sanitization[C]//Proceedings of the Association for Computational Linguistics.Association for Computational Linguistics,2021.
[84] YU D,NAIK S,BACKURS A,et al.Differentially Private Fine-tuning of Language Models[J].Journal of Privacy and Confidentiality,2024,14(2).
[85] TORKZADEHMAHANI R,KAIROUZ P,PATEN B.DP-CGAN:Differentially Private Synthetic Data and Label Generation[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2019.
[86] WANG H,PANG S,LU Z,et al.dp-promise:Differentially Private Diffusion Probabilistic Models for Image Synthesis[C]//Proceedings of the 33rd USENIX Conference on Security Symposium.USENIX,2024.
[87] NIU B,CHEN Y,WANG B,et al.AdaPDP:Adaptive Persona-lized Differential Privacy[C]//Proceedings of IEEE INFOCOM 2021-IEEE Conference on Computer Communications.2021.
[88] LI Y,SONG X,TU Y,et al.GAPBAS:Genetic Algorithm-Based Privacy Budget Allocation Strategy in Differential PrivacyK-Means Clustering Algorithm[J].Computers & Security,2024,139:103697.
[89] LIU Y H,CHEN H,LIU Y X,et al.State-of-the-Art PrivacyAttacks and Defenses on Graphs[J].Chinese Journal of Computers,2022,45(4):702-734.
[90] LEE J,CLIFTON C.How Much Is Enough? Choosing ε for Differential Privacy[C]//Information Security.Berlin:Springer,2011.
[91] NALDI M,D’ACQUISTO G.Differential Privacy:An Estimation Theory-Based Method for Choosing Epsilon[J].arXiv:1510.00917,2015.
[92] WAN S,LI F H,NIU B,et al.Research Progress on Location Privacy-Preserving Techniques[J].Journal on Communications,2016,37(12):124-141.
[93] PENG C G,DING H F,ZHU Y J,et al.Information EntropyModels and Privacy Metrics Methods for Privacy Protection[J].Ruan Jian Xue Bao/Journal of Software,2016,27(8):1891-1903.
[94] KOTELNIKOV A,BARANCHUK D,RUBACHEV I,et al.TabDDPM:Modelling Tabular Data with Diffusion Models[C]//Proceedings of the 40th International Conference on Machine Learning.JMLR.org,2023.
[95] CHOI E,BISWAL S,MALIN B,et al.Generating Multi-labelDiscrete Patient Records Using Generative Adversarial Networks[C]//Proceedings of the 2nd Machine Learning for Healthcare Conference.PMLR,2017:286-305.
[96] ABDI H.The Kendall Rank Correlation Coefficient[M]//Encyclopedia of Measurement and Statistics.Thousand Oaks:SAGE Publications,2007:508-510.
[97] ZHANG H,ZHANG J,SRINIVASAN B,et al.Mixed-TypeTabular Data Synthesis with Score-Based Diffusion in Latent Space[J].arXiv:2310.09656,2023.
[98] DE WINTER J C,GOSLING S D,POTTER J.Comparing the Pearson and Spearman Correlation Coefficients across Distributions and Sample Sizes:A Tutorial Using Simulations and Empirical Data[J].Psychological Methods,2016,21(3):273.
[99] ANDRÉS M E,BORDENABE N E,CHATZIKOKOLAKIS K,et al.Geo-Indistinguishability:Differential Privacy for Location-Based Systems[C]//Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security.ACM,2013:901-914.
[100] WANG L,QIN G,YANG D,et al.Geographic Differential Privacy for Mobile Crowd Coverage Maximization[C]//Procee-dings of the Thirty-Second AAAI Conference on Artificial Intelligence.New Orleans:AAAI,2018.
[101] YALE A,DASH S,DUTTA R,et al.Generation and Evaluation of Privacy Preserving Synthetic Health Data[J].Neurocompu-ting,2020,416:244-255.
[102] LIU Y,ACHARYA U R,TAN J H.Preserving Privacy inHealthcare:A Systematic Review of Deep Learning Approaches for Synthetic Data Generation[J].Computer Methods and Programs in Biomedicine,2025,260:108571.
[103] ABOWD J M.The U.S.Census Bureau Adopts Differential Privacy[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.ACM,2018:2867.
[104] DWORK C.Differential Privacy and the US Census[C]//Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems.ACM,2019:1.
[105] FICEK J,WANG W,CHEN H,et al.Differential Privacy inHealth Research:A Scoping Review[J].Journal of the American Medical Informatics Association,2021,28(10):2269-2276.
[106] CHEN J H,WANG Y J,TSAI Y C,et al.Financial Vision Based Differential Privacy Applications[J].arXiv:2112.14075,2021.
[107] WU L,QIN C,XU Z,et al.TCPP:Achieving Privacy-Preserving Trajectory Correlation with Differential Privacy[J].IEEE Transactions on Information Forensics and Security,2023,18:4006-4020.
[108] ZHAO Y Q,YANG M.Review of Differential Privacy Research[J].Computer Science,2023,50(4):265-276.
[109] WANG M Y,ZHANG X,LI W J,et al.Review of Research on Privacy Protection Technology for Data Publication[J].Journal of Chinese Computer Systems,2020,41(12):2657-2667.
[110] WANG Y,LUO C H,DENG J Z,et al.Matrix FactorizationRecommendation Algorithm Based on Local Differential Privacy with Objective Perturbation[J].Chinese Journal of Computers,2025,48(2):451-462.
[111] MAO Y,YE Q,WANG Q,et al.Differential Privacy for Time Series:A Survey[J].IEEE Data Engineering Bulletin,2024,48:67-92.
[112] DEMELIUS L,KERN R,TRÜGLER A.Recent Advances ofDifferential Privacy in Centralized Deep Learning:A Systematic Survey[J].ACM Computing Surveys,2025,57(6):Article 158.
[113] ZHOU X,HU Y,JIA Q,et al.LLM-Enabled Multi-Modal Data Synthesis via Cross-Domain Collaboration[J].IEEE Communications Magazine,2025,63(10):86-93.
[114] LEE P.Synthetic Data and the Future of AI[J].Cornell Law Review,2025,110:1.
[1] WU Shuiqing, QIU Jihao, LIU Xiang, DONG Yuxi, WEN Yimin. Proxy-based Source-free Domain Adaptation for EEG Emotion Recognition Method [J]. Computer Science, 2026, 53(8): 94-102.
[2] HUANG Shoumeng, YANG Boxiong, YANG Ming. Signature Scheme Based on Traceable Attributes [J]. Computer Science, 2026, 53(6A): 250300121-8.
[3] LIU Suyi, LIU Qi, GAO Weibo. Agent4Stu:Efficient LLM-based Student Answer Behavior Simulation Agent [J]. Computer Science, 2026, 53(4): 347-355.
[4] ZHANG Huan, HOU Mingxing, LIU Guangna , SHI Ying. Fine-grained Access Control Model for Big Data Based on Dynamic Data Sensitivity Levels [J]. Computer Science, 2026, 53(2): 187-195.
[5] LI Jiahui, LI Yinglong, CHEN Tieming. Privacy-preserving Computation in Edge Service Scenario of Internet of Vehicles:A Review ofTechnical Basis and Research Progress [J]. Computer Science, 2026, 53(1): 298-322.
[6] LUO Yanjie, LI Lin, WU Xiaohua, LIU Jia. Happiness Prediction Approach via Adaptive Privacy Budget Allocation [J]. Computer Science, 2025, 52(7): 372-378.
[7] CHEN Yanfeng, FENG Zhiwei, DENG Qingxu, WANG Yan. Survey of Sensor Attack Defense Strategies for Cyber Physical Systems [J]. Computer Science, 2025, 52(4): 4-13.
[8] WANG Xin, CHEN Kun, SUN Lingyun. Research on Foundation Model Methods for Addressing Non-IID Issues in Federated Learning [J]. Computer Science, 2025, 52(12): 302-313.
[9] LI Jincheng, LI Yingna, FU Guoqing. Design and Application of Decision Tree Algorithms for Privacy-preserving [J]. Computer Science, 2025, 52(11A): 241200115-9.
[10] XIE Zhenjie, LIU Yiming, YIN Xiaokang, LIU Shengli, ZHANG Yongguang. Additively Homomorphic Encryption Scheme Based on Domestic Cryptographic Algorithm SM9 [J]. Computer Science, 2025, 52(11): 408-414.
[11] HE Yuyu, ZHOU Feng, TIAN Youliang, XIONG Wei, WANG Shuai. Lightweight Privacy-preserving Mobile Sensing Classification Framework Based on AddictiveSecret Sharing [J]. Computer Science, 2025, 52(11): 415-424.
[12] WANG Xin, XIONG Shubo, SUN Lingyun. Federated Graph Learning:Problems,Methods and Challenges [J]. Computer Science, 2025, 52(1): 362-373.
[13] WANG Jinghong, TIAN Changshen, LI Haokang, WANG Wei. Lagrangian Dual-based Privacy Protection and Fairness Constrained Method for Few-shot Learning [J]. Computer Science, 2024, 51(7): 405-412.
[14] SUN Jianming, ZHAO Mengxin. Survey of Application of Differential Privacy in Edge Computing [J]. Computer Science, 2024, 51(6A): 230700089-9.
[15] SUN Min, DING Xining, CHENG Qian. Federated Learning Scheme Based on Differential Privacy [J]. Computer Science, 2024, 51(6A): 230600211-6.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!