Improving Data Utility Through Game Theory in Personalized Differential Privacy

Calibrating Noise to Sensitivity in Private Data Analysis

Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272 · see citing works

Differential Privacy

Dwork, C. (2006) · Lecture notes in computer science · doi:10.1007/11787006_1 · Cited by 5,945 · see citing works

<i>L</i> -diversity: Privacy beyond <i>k</i> -anonymity

Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217302 · Cited by 3,617 · see citing works

SecureML: A System for Scalable Privacy-Preserving Machine Learning

Mohassel, P., & Zhang, Y. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.12 · Cited by 1,936 · see citing works

Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression

Samarati, P., & Sweeney, L. (2018) · doi:10.1184/r1/6625469 · Cited by 1,341 · see citing works

A Statistical Framework for Differential Privacy

Wasserman, L., & Zhou, S. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.tm08651 · Cited by 398 · see citing works

Differentially Private Data Publishing and Analysis: A Survey

Zhu, T., Li, G., Zhou, W., & Yu, P. S. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2697856 · Cited by 330 · see citing works

IEEE Transactions on Dependable and Secure Computing

Girault, A., & Kalla, H. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.8858 · Cited by 309 · see citing works

Big privacy: challenges and opportunities of privacy study in the age of big data

Yu, S. (2016) · IEEE Access · doi:10.1109/access.2016.2577036 · Cited by 307 · see citing works

Conservative or liberal? Personalized differential privacy

Jorgensen, Z., Yu, T., & Cormode, G. (2015) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2015.7113353 · Cited by 232 · see citing works

Networking for Big Data: A Survey

Yu, S., Liu, M., Dou, W., Liu, X., & Zhou, S. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2610963 · Cited by 220 · see citing works

Real-time and Spatio-temporal Crowd-sourced Social Network Data Publishing with Differential Privacy

Wang, Q., Yan, Z., Lu, X. L., Wang, Z., Qin, Z., & Ren, K. (2016) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2016.2599873 · Cited by 196 · see citing works

Correlated Differential Privacy: Hiding Information in Non-IID Data Set

Zhu, T., Xiong, P., Li, G., & Zhou, W. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2368363 · Cited by 173 · see citing works

Latent-Data Privacy Preserving With Customized Data Utility for Social Network Data

He, Z., Cai, Z., & Yu, J. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2738018 · Cited by 168 · see citing works

Privacy Preserving Social Network Data Publication

Abawajy, J., Ninggal, M. I. H., & Herawan, T. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2533668 · Cited by 136 · see citing works

Hummingbird: Privacy at the Time of Twitter

Cristofaro, E. D., Soriente, C., Tsudik, G., & Williams, A. (2012) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2012.26 · Cited by 112 · see citing works

Privacy of Things: Emerging Challenges and Opportunities in Wireless Internet of Things

Qu, Y., Yu, S., Zhou, W., Peng, S., Wang, G., & Xiao, K. (2018) · IEEE Wireless Communications · doi:10.1109/mwc.2017.1800112 · Cited by 98 · see citing works

Scoring Users’ Privacy Disclosure Across Multiple Online Social Networks

Aghasian, E., Garg, S., Gao, L., Yu, S., & Montgomery, J. (2017) · IEEE Access · doi:10.1109/access.2017.2720187 · Cited by 89 · see citing works

Fool Me If You Can: Mimicking Attacks and Anti-Attacks in Cyberspace

Yu, S., Guo, S., & Stojmenović, I. (2013) · IEEE Transactions on Computers · doi:10.1109/tc.2013.191 · Cited by 85 · see citing works

Secure and Efficient Time Synchronization in Heterogeneous Sensor Networks

Du, X., Guizani, M., Xiao, Y., & Chen, H. (2008) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2007.912327 · Cited by 84 · see citing works

Leaking privacy and shadow profiles in online social networks

García, D. (2017) · Science Advances · doi:10.1126/sciadv.1701172 · Cited by 84 · see citing works

A Hybrid Privacy Protection Scheme in Cyber-Physical Social Networks

Qu, Y., Yu, S., Gao, L., Zhou, W., & Peng, S. (2018) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2018.2861775 · Cited by 65 · see citing works

A Feasible IP Traceback Framework through Dynamic Deterministic Packet Marking

Yu, S., Zhou, W., Guo, S., & Guo, M. (2015) · IEEE Transactions on Computers · doi:10.1109/tc.2015.2439287 · Cited by 56 · see citing works

A Utility-Optimized Framework for Personalized Private Histogram Estimation

Nie, Y., Yang, W., Huang, L., Xie, X., Zhao, Z., & Wang, S. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2841360 · Cited by 54 · see citing works

Catch me in the dark: Effective privacy-preserving outsourcing of feature extractions over image data

Wang, Q., Hu, S., Ren, K., Wang, J., Wang, Z., & Du, M. (2016) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2016.7524460 · Cited by 42 · see citing works

Privacy Preservation for Context Sensing on Smartphone

Wang, W., & Zhang, Q. (2016) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2015.2512301 · Cited by 36 · see citing works

Personalized location privacy in mobile networks: A social group utility approach

Gong, X., Chen, X., Xing, K., Shin, D., Zhang, M., & Zhang, J. (2015) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2015.7218473 · Cited by 35 · see citing works

Diffusing Private Data Over Networks

Koufogiannis, F., & Pappas, G. J. (2016) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2016.7525223 · Cited by 35 · see citing works

PIF: A Personalized Fine-Grained Spam Filtering Scheme With Privacy Preservation in Mobile Social Networks

Zhang, K., Liang, X., Lu, R., & Shen, X. (2015) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2016.2519819 · Cited by 32 · see citing works

Scalable privacy-preserving big data aggregation mechanism

Wu, D., Yang, B., & Wang, R. (2016) · Digital Communications and Networks · doi:10.1016/j.dcan.2016.07.001 · Cited by 31 · see citing works

Big data set privacy preserving through sensitive attribute-based grouping

Qu, Y., Yu, S., Gao, L., & Niu, J. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7997113 · Cited by 27 · see citing works

Personalized Privacy-Preserving Data Aggregation for Histogram Estimation

Wang, S., Huang, L., Tian, M., Yang, W., Xu, H., & Guo, H. (2015) · Globecom · doi:10.1109/glocom.2015.7417364 · Cited by 27 · see citing works

Authenticated Data Redaction with Fine-Grained Control

Ma, J., Liu, J., Huang, X., Xiang, Y., & Wu, W. (2017) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2017.2754646 · Cited by 24 · see citing works

Investigating the Privacy versus Forwarding Accuracy Tradeoff in OpportunisticInterest-Casting

Costantino, G., Martinelli, F., & Santi, P. (2013) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2013.20 · Cited by 20 · see citing works

Improving Data Utility through Game Theory in Personalized Differential Privacy

Qu, Y., Cui, L., Yu, S., Zhou, W., & Wu, J. (2018) · International Conference on Communications · doi:10.1109/icc.2018.8422735 · Cited by 7 · see citing works

Machine Unlearning: A Survey

Xu, H., Zhu, T., Zhang, L., Zhou, W., & Yu, P. S. (2024) · ACM Computing Surveys · doi:10.1145/3603620 · Cited by 164 · see citing works

Applications of Differential Privacy in Social Network Analysis: A Survey

Jiang, H., Pei, J., Yu, D., Yu, J., Gong, B., & Cheng, X. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3073062 · Cited by 140 · see citing works

Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

Shen, Y., Shen, S., Li, Q., Zhou, H., Wu, Z., & Qu, Y. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.05.004 · Cited by 103 · see citing works

Optimal privacy preservation strategies with signaling Q-learning for edge-computing-based IoT resource grant systems

Shen, S., Wu, X., Sun, P. J., Zhou, H., Wu, Z., & Yu, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120192 · Cited by 74 · see citing works

A Hybrid Privacy Protection Scheme in Cyber-Physical Social Networks

Qu, Y., Yu, S., Gao, L., Zhou, W., & Peng, S. (2018) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2018.2861775 · Cited by 65 · see citing works

AdaPDP: Adaptive Personalized Differential Privacy

Niu, B., Chen, Y., Wang, B., Wang, Z., Li, F., & Cao, J. P. (2021) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom42981.2021.9488825 · Cited by 54 · see citing works

An approach of flow compensation incentive based on Q-Learning strategy for IoT user privacy protection

Chen, L., Zhang, D., Zhang, J., Zhang, T., Du, J., & Fan, H. (2022) · AEU - International Journal of Electronics and Communications · doi:10.1016/j.aeue.2022.154172 · Cited by 54 · see citing works

MSDP: multi-scheme privacy-preserving deep learning via differential privacy

Owusu-Agyemeng, K., Qin, Z., Xiong, H., Liu, Y., Zhuang, T., & Qin, Z. (2021) · Personal and Ubiquitous Computing · doi:10.1007/s00779-021-01545-0 · Cited by 33 · see citing works

Utility-aware Exponential Mechanism for Personalized Differential Privacy

Niu, B., Chen, Y., Wang, B., Cao, J. P., & Li, F. (2020) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc45663.2020.9120532 · Cited by 27 · see citing works

Personalized Location Privacy With Road Network-Indistinguishability

Ma, B., Wang, X., Ni, W., & Liu, R. P. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3179501 · Cited by 24 · see citing works

Game-theoretic analytics for privacy preservation in Internet of Things networks: A survey

Shen, Y., Shepherd, C., Ahmed, C. M., Shen, S., Wu, X., Ke, W., & Yu, S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108449 · Cited by 16 · see citing works

User-Driven Privacy-Preserving Data Streams Release for Multi-Task Assignment in Mobile Crowdsensing

Li, Z., Wu, J., Long, S., Zheng, Z., Li, C., & Dong, M. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3516885 · Cited by 13 · see citing works

Privately Publishing Internet of Things Data: Bring Personalized Sampling Into Differentially Private Mechanisms

Huang, W. C., Zhou, S., Zhu, T., & Liao, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3089518 · Cited by 12 · see citing works

The Trade-off Between Privacy and Utility in Local Differential Privacy

Li, M., Tian, Y., Zhang, J., Fan, D., & Zhao, D. (2021) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana53684.2021.00071 · Cited by 10 · see citing works

Privacy Issues in Edge Computing

Gao, L., Luan, T. H., Gu, B., Qu, Y., & Xiang, Y. (2021) · Wireless networks · doi:10.1007/978-981-16-2199-4_2 · Cited by 10 · see citing works

Differential private knowledge trading in vehicular federated learning using contract theory

Zhang, J., Wang, C., & Li, S. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111356 · Cited by 7 · see citing works

Green Resilience of Cyber-Physical Systems

Rimawi, D. (2022) · IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) · doi:10.1109/issrew55968.2022.00048 · Cited by 5 · see citing works

Non-Technical Losses Detection in Smart Grids: An Ensemble Data-Driven Approach

Xing, Y., Guo, L., Xie, Z., Cui, L., Gao, L., & Yu, S. (2020) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads51040.2020.00078 · Cited by 3 · see citing works

A differential privacy protection query language for medical data: a proof-of-concept system validation

Wang, H., Zhang, Y., Yin, H., Li, R., & Wu, X. (2021) · Journal of Bio-X Research · doi:10.1097/jbr.0000000000000099 · Cited by 2 · see citing works

A Personalized Preservation Mechanism Satisfying Local Differential Privacy in Location-Based Services

Wu, D., Wu, X., Gao, J., Ji, G., Xu, X., Qi, L., & Dou, W. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-9129-7_12 · Cited by 2 · see citing works

Sensitive Information Protection Model Based on Bayesian Game

Liu, Y., Liu, Z., Wang, X., Yang, Q. P., Zuo, G., & Jiang, F. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.029002 · Cited by 1 · see citing works

Personalized Differentially Private Online Minimum Bipartite Matching in Spatial Crowdsourcing

Lv, C., Zhang, L., & Li, X. (2022) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom57025.2022.00025 · Cited by 1 · see citing works

A Differential Privacy Protection Query Language for Medical Data: Study Design (Preprint)Preprint

Wang, H., Wu, X., Tan, Y., Yin, H., Cheng, X., Li, Z., Zhang, Y., & Shi, M. (2020) · doi:10.2196/preprints.23073

Performance of RSA Algorithm Using Game Theory for Aadhaar Card

Lizy, R. F. S., & Raj, V. J. (2023) · Advances in intelligent systems and computing · doi:10.1007/978-981-19-5443-6_7

PPVC: Towards a Personalized Local Differential Privacy-Preserving Scheme for V2G Charging Networks

Qin, P., & Wang, L. (2023) · Mathematics · doi:10.3390/math11204257

Optimizing Federated Learning with Local Differential Privacy: A Game-Theoretic Approach for Privacy-Utility TradeoffPreprint

Zeng, Q., & Yin, C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4862090

A Customised Privacy Preservation Mechanism for Cyber‐Physical Systems

Yogi, M. K., & Chakravarthy, A. S. N. (2025) · doi:10.1002/9781394268917.ch4

Exploring Configurational Factors Influencing Online Privacy Protection Behaviors of Internet Users

Liu, H., & Zhang, Y. (2025) · Asia Pacific Economic and Management Review · doi:10.62177/apemr.v1i2.269

A General Framework for Per-record Differential Privacy

Chen, X., Sun, D., Xu, Q., & Dong, W. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3769752

Personalized Differential Privacy for Support Vector Machines

Wang, X., Liu, X., & Xu, W. (2026) · Journal of Systems Science and Complexity · doi:10.1007/s11424-026-5104-0

Privacy preserving distributed data processing and game theoretic methods for data utility and privacy tradeoffs

Wu, N. (2019) · Figshare · doi:10.25949/19427621

Research a New Tradeoff Method Between Privacy and Utility in Differential privacyPreprint

Sun, P. J. (2021) · Research Square · doi:10.21203/rs.3.rs-579007/v1

Conservative or liberal? Personalized differential privacy

Jorgensen, Z., Yu, T., & Cormode, G. (2015) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2015.7113353 · Cited by 232 · see citing works

Automatic Discovery of Privacy–Utility Pareto Fronts

Avent, B., González, J., Diethe, T., Paleyes, A., & Balle, B. (2020) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2020-0060 · Cited by 3 · see citing works

Differential privacy as a tool for truthfulness in games

Cummings, R. (2017) · XRDS Crossroads The ACM Magazine for Students · doi:10.1145/3123742 · Cited by 2 · see citing works