The fast development of the Internet and mobile devices results in a crowdsensing business model, where individuals (users) are willing to contribute their data to help the institution (data collector) analyze and release useful information. However, the reveal of personal data will bring huge privacy threats to users, which will impede the wide application of the crowdsensing model. To settle the problem, the definition of local differential privacy (LDP) is proposed. Afterwards, to respond to the varied privacy preference of users, researchers propose a new model, i.e., personalized local differential privacy (PLDP), which allow users to specify their own privacy parameters. In this paper, we focus on a basic task of calculating the mean value over a single numeric attribute with PLDP. Based on the previous schemes for mean estimation under LDP, we employ PLDP model to design novel schemes (LAP, DCP, PWP) to provide personalized privacy for each user. We then theoretically analysis the worst-case variance of three proposed schemes and conduct experiments on synthetic and real datasets to evaluate the performance of three methods. The theoretical and experimental results show the optimality of PWP in the low privacy regime and a slight advantage of DCP in the high privacy regime.
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Kasiviswanathan S P, Lee H K, Nissim K, Raskhodnikova S. What can we learn privately? Siam Journal on Computing, 2008, 40(3): 793–826
Dwork C. Differential privacy. In: Proceedings of International Conference on Automata, Languages and Programming. 2006, 1–12
Duchi J C, Jordan M I, Wainwright M J. Minimax optimal procedures for locally private estimation. Journal of the American Statistical Association, 2018, 113(521): 182–201
Wang N, Xiao X, Yang Y, Zhao J, Hui S, Shin H, Shin J, Yu G. Collecting and Analyzing Multidimensional Data with Local Differential Privacy. In: Proceedings of the 35th IEEE Annual International Conference on Data Engineering. 2019, 638–649
Chen R, Li H, Qin A K, Kasiviswanathan S P, Jin H. Private spatial data aggregation in the local setting. In: Proceedings of the 32nd IEEE International Conference on Data Engineering. 2016, 289–300
Dwork C, McSherry F, Nissim K, Smith A. Calibrating noise to sensitivity in private data analysis. In: Proceedings of the 3rd Theory of Cryptography Conference. 2006, 265–284
Liu Y, Zhao Q. E-Voting scheme using secret sharing and K-anonymity. World Wide Web: Internet and Web Information Systems, 2019, 22(4): 1657–1667
Xu C, Ren J, Zhang D, Zhang Y. Distilling at the edge: a local differential privacy obfuscation framework for IoT data analytics. IEEE Communications Magazine, 2018, 56(8): 20–25
Zhang Y, Huang H, Yang L, Xiang Y, Li M. Serious challenges and potential solutions for the industrial Internet of Things with edge intelligence. IEEE Network, 2020, 33(5): 41–45
Kuang B, Fu A, Yu S, Yang G, Su M, Zhang Y. ESDRA: an efficient and secure distributed remote attestation scheme for IoT swarms. IEEE Internet of Things Journal, 2019, 6(5): 8372–8383
Li N, Qardaji W, Dong S, Cao J. Privbasis: frequent itemset mining with differential privacy. Proceedings of the VLDB Endowment, 2012, 5(11): 1340–1351
Su S, Xu S, Cheng X, Li Z, Yang F. Differentially private frequent itemset mining via transaction splitting. IEEE Transactions on Knowledge Data Engineering, 2015, 27(7): 1875–1891
Zhu Y, Zhang Y, Li X, Yan H, Li J. Improved collusion-resisting secure nearest neighbor query over encrypted data in cloud. Concurrency and Computation Practice and Eperience, 2019, 31(21): e4681
Zhu Y, Li X. Privacy-preserving k-means clustering with local synchronization in peer-to-peer networks. Peer-to-Peer Networking and Applications, 2020, 13(6): 2272–2284
Sarwate A D, Chaudhuri K. Signal processing and machine learning with differential privacy: algorithms and challenges for continuous data. IEEE Signal Processing Magazine, 2013, 30(5): 86–94
Ji Z, Lipton Z C, Elkan C. Differential privacy and machine learning: a survey and review. 2014, arXiv preprint, arXiv: 1412.7584
Zhang Y, Xiao X, Yang L, Xiang Y, Zhong S. Secure and efficient outsourcing of PCA-dased face recognition. IEEE Transactions on Information Forensics and Security, 2020, 15(1): 1683–1695
Song J, Liu Y, Shao J, Tang C. A dynamic membership data aggregation (DMDA) protocol for smart grid. IEEE Systems Journal, 2020, 14(1): 900–908
Chen J, Liu G, Liu Y. Lightweight privacy-preserving raw data publishing scheme. IEEE Transactions on Emerging Topics in Computing, 2020, DOI: https://doi.org/10.1109/TETC.2020.2974183
Fu A, Yu S, Zhang Y, Wang H, Huang C. NPP: a new privacy-aware public auditing scheme for cloud data sharing with group users. IEEE Transactions on Big Data, 2017, DOI: https://doi.org/10.1109/TBDATA.2017.2701347
Erlingsson Ú, Pihur V, Korolova A. Rappor: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of ACM Sigsac Conference on Computer and Communications Security. 2014, 1054–1067
Kairouz P, Bonawitz K, Ramage D. Discrete distribution estimation under local privacy. In: Proceedings of International Conference on Machine learning. 2016, 2436–2444
Ye M, Barg A. Optimal schemes for discrete distribution estimation under locally differential privacy. IEEE Transactions on Information Theory, 2018, 64(8): 5662–5676
Qin Z, Yang Y, Yu T, Kjalil I, Xiao X, Ren K. Heavy hitter estimation over set-valued data with local differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 2016, 192–203
Wang T, Blocki J, Li N, Jha S. Locally differentially private protocols for frequency estimation. In: Proceedings of the 26th USENIX Security Symposium. 2017, 729–745
Ye Q, Hu H, Meng X, Zheng H. PrivKV: key-value data collection with local differential privacy. In: Proceedings of IEEE Symposium on Security and Privacy. 2019, 294–308
Xue Q, Zhu Y, Wang J. Joint distribution estimation and naive bayes classification under local differential privacy. IEEE Transactions on Emerging Topics in Computing, 2019, DOI: https://doi.org/10.1109/TETC.2019.2959581
Xue Q, Zhu Y, Wang J, Li X. Distributed set intersection and union with local differential privacy, In: Proceedings of IEEE International Conference on Parallel & Distributed Systems. 2017, 198–205
Xue Q, Zhu Y, Wang J, Li X, Zhang J. Locally differentially private distributed algorithms for set intersection and union. Science China Information Sciences, 2021, 64: 219101
Warner S L. Randomized response: a survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 1965, 60(309): 63–66
Jorgensen Z, Yu T, Cormode G. Conservative or liberal? Personalized differential privacy. In: Proceedings of the 32nd IEEE International Conference on Data Engineering. 2016, 1023–1034
Wang S, Huang L, Tian M, Yang W, Xu H, Guo H. Personalized privacy-preserving data aggregation for histogram estimation. In: Proceedings of 2015 IEEE Global Communications Conference. 2015, 1–6
Ye Y, Zhang M, Feng D, Li H, Chi J. Multiple privacy regimes mechanism for local differential privacy. In: Proceedings of International Conference on Database Systems for Advanced Applications. 2019, 247–263
Murakami T, Kawamoto Y. Utility-optimized local differential privacy mechanisms for distribution estimation. In: Proceedings of the 28th USENIX Security Symposium. 2019, 1877–1894
This work was partly supported by the National Key Research and Development Program of China (2020YFB1005900), the Research Fund of Guangxi Key Laboratory of Trusted Software (kx202034), the Team Project of Collaborative Innovation in Universities of Gansu Province (2017C-16) and Collaborative Innovation Center of Novel Software Technology and Industrialization.
Qiao Xue received her BE degree and PhD degree in Nanjing University of Aeronautics and Astronautics, China in 2015 and 2020, respectively. She is currently a postdoctoral fellow in Hong Kong Polytechnic University, China. Her research interests include information security and data privacy.
Youwen Zhu received his BE degree and PhD degree in Computer Science from University of Science and Technology of China, China in 2007 and 2012, respectively. From 2012 to 2014, he is a JSPS postdoctor in Kyushu University, Japan. He is currently a Professor at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China. He has published more than 40 papers in refereed international conferences and journals, and has served as program committee member in several international conferences. His research interests include identity authentication, information security and data privacy.
Jian Wang received the PhD degree in Nanjing University, China in 1998. He is currently a Professor at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China. His research interests include cryptographic protocol and malicious tracking.
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Xue, Q., Zhu, Y. & Wang, J. Mean estimation over numeric data with personalized local differential privacy. Front. Comput. Sci. 16, 163806 (2022). https://doi.org/10.1007/s11704-020-0103-0