Processing math: 100%
We use cookies to improve your experience with our site.

Indexed in:

SCIE, EI, Scopus, INSPEC, DBLP, CSCD, etc.

Submission System
(Author / Reviewer / Editor)
Lei Cui, Youyang Qu, Mohammad Reza Nosouhi, Shui Yu, Jian-Wei Niu, Gang Xie. Improving Data Utility Through Game Theory in Personalized Differential Privacy[J]. Journal of Computer Science and Technology, 2019, 34(2): 272-286. DOI: 10.1007/s11390-019-1910-3
Citation: Lei Cui, Youyang Qu, Mohammad Reza Nosouhi, Shui Yu, Jian-Wei Niu, Gang Xie. Improving Data Utility Through Game Theory in Personalized Differential Privacy[J]. Journal of Computer Science and Technology, 2019, 34(2): 272-286. DOI: 10.1007/s11390-019-1910-3

Improving Data Utility Through Game Theory in Personalized Differential Privacy

Funds: This work was supported by the Shanxi International Cooperation Project under Grant No. 201803D421039, the China Scholarship Council (CSC) under Grant No. 201708240007, and the China Scholarship Council (CSC) under Grant No. 201808240004.
More Information
  • Author Bio:

    Lei Cui received his B.S. degree in electrical engineering from Taiyuan University of Technology, Taiyuan, in 2010. He is currently pursuing his Ph.D. degree at the School of Information and Computer, Taiyuan University of Technology, Taiyuan. His research interests include security and privacy issues in the IoT, social networks, and big data.

  • Corresponding author:

    Gang Xie E-mail: xiegang@tyut.edu.cn

  • Received Date: June 27, 2018
  • Revised Date: January 26, 2019
  • Published Date: March 04, 2019
  • Due to dramatically increasing information published in social networks, privacy issues have given rise to public concerns. Although the presence of differential privacy provides privacy protection with theoretical foundations, the trade-off between privacy and data utility still demands further improvement. However, most existing studies do not consider the quantitative impact of the adversary when measuring data utility. In this paper, we firstly propose a personalized differential privacy method based on social distance. Then, we analyze the maximum data utility when users and adversaries are blind to the strategy sets of each other. We formalize all the payoff functions in the differential privacy sense, which is followed by the establishment of a static Bayesian game. The trade-off is calculated by deriving the Bayesian Nash equilibrium with a modified reinforcement learning algorithm. The proposed method achieves fast convergence by reducing the cardinality from n to 2. In addition, the in-place trade-off can maximize the user's data utility if the action sets of the user and the adversary are public while the strategy sets are unrevealed. Our extensive experiments on the real-world dataset prove the proposed model is effective and feasible.
  • [1]
    Garcia D. Leaking privacy and shadow profiles in online social networks. Science Advances, 2017, 3(8): Article No. e1701172.
    [2]
    He Z, Cai Z, Yu J. Latent-data privacy preserving with customized data utility for social network data. IEEE Transactions on Vehicular Technology, 2018, 67(1): 665-673.
    [3]
    Cristofaro E D, Soriente C, Tsudik G, Williams A. Hummingbird: Privacy at the time of twitter. In Proc. the 2012 IEEE Symposium on Security and Privacy, May 2012, pp.285-299.
    [4]
    Abawajy J H, Ninggal M I H, Herawan T. Privacy preserving social network data publication. IEEE Communications Surveys and Tutorials, 2016, 18(3): 1974-1997.
    [5]
    Yu S, Zhou W, Guo S, Guo M. A feasible IP traceback framework through dynamic deterministic packet marking. IEEE Transactions on Computers, 2016, 65(5): 1418-1427.
    [6]
    Qu Y, Yu S, Gao L, Zhou W, Peng S. A hybrid privacy protection scheme in cyber-physical social networks. IEEE Transactions on Computational Social Systems, 2018, 5(3): 773-784.
    [7]
    Qu Y, Yu S, Zhou W, Peng S, Wang G, Xiao K. Privacy of things: Emerging challenges and opportunities in wireless Internet of Things. IEEE Wireless Communications, 2018, 25(6): 91-97.
    [8]
    Yu S, Liu M, Dou W, Liu X, Zhou S. Networking for big data: A survey. IEEE Communications Surveys and Tutorials, 2017, 19(1): 531-549.
    [9]
    Zhu T, Xiong P, Li G, Zhou W. Correlated differential privacy: Hiding information in non-ⅡD data set. IEEE Transactions on Information Forensics and Security, 2015, 10(2): 229-242.
    [10]
    Koufogiannis F, Pappas G J. Diffusing private data over networks. IEEE Transactions on Control of Network Systems, 2016, 5(3): 1027-1037.
    [11]
    Wang W, Zhang Q. Privacy preservation for context sensing on smartphone. IEEE/ACM Transactions on Networking, 2016, 24(6): 3235-3247.
    [12]
    Yu S. Big privacy: Challenges and opportunities of privacy study in the age of big data. IEEE Access, 2016, 4: 2751- 2763.
    [13]
    Mohassel P, Zhang Y. SecureML: A system for scalable privacy-preserving machine learning. In Proc. the 2017 IEEE Symposium on Security and Privacy, May 2017, pp.19-38.
    [14]
    Costantino G, Martinelli F, Santi P. Investigating the privacy versus forwarding accuracy tradeoff in opportunisticinterest-casting. IEEE Transactions on Mobile Computing, 2014, 13(4): 824-837.
    [15]
    Pierangela S, Latanya S. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. https://dataprivacylab.org/dataprivacy/projects/kanonymity/paper3.pdf,May2018.
    [16]
    Machanavajjhala A, Kifer D, Gehrke J, VenkitasubraManiam M. L-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data, 2007, 1(1): Article No. 3.
    [17]
    Gong X, Chen X, Xing K, Shin D, Zhang M, Zhang J. Personalized location privacy in mobile networks: A social group utility approach. In Proc. the 2005 IEEE Conference on Computer Communications, April 2015, pp.1008-1016.
    [18]
    Dwork C. Differential privacy. In Proc. the 33rd International Colloquium on Automata, Languages and Programming, July 2006, pp.1-12.
    [19]
    Zhu T, Li G, Zhou W, Yu P S. Differentially private data publishing and analysis: A survey. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(8): 1619-1638.
    [20]
    Wang Q, Zhang Y, Lu X, Wang Z, Qin Z, Ren K. Realtime and spatio-temporal crowd-sourced social network data publishing with differential privacy. IEEE Transactions on Dependable and Secure Computing, 2016, 15(4): 591-606.
    [21]
    Zhang K, Liang X, Lu R, Shen X. PIF: A personalized finegrained spam filtering scheme with privacy preservation in mobile social networks. IEEE Transactions on Computational Social Systems, 2015, 2(3): 41-52.
    [22]
    Yu S, Guo S, Stojmenovic I. Fool me if you can: Mimicking attacks and anti-attacks in cyberspace. IEEE Transactions on Computers, 2015, 64(1): 139-151.
    [23]
    Qu Y, Cui L, Yu S, Zhou W, Wu J. Improving data utility through game theory in personalized differential privacy. In Proc. the 2018 IEEE International Conference on Communications, May 2018, Article No. 656.
    [24]
    Wu D, Yang B, Wang R. Scalable privacy-preserving big data aggregation mechanism. Digital Communications and Networks, 2016, 2(3): 122-129.
    [25]
    Wang Q, Hu S, Ren K, Wang J, Wang Z, Du M. Catch me in the dark: Effective privacy-preserving outsourcing of feature extractions over image data. In Proc. the 35th Annual IEEE International Conference on Computer Communications, April 2016, Article No. 131.
    [26]
    Ma J, Liu J, Huang X, Xiang Y, Wu W. Authenticated data redaction with fine-grained control. IEEE Transactions on Emerging Topics in Computing. doi: 10.1109/TETC.2017.2754646.
    [27]
    Qu Y, Yu S, Gao L, Niu J. Big data set privacy preserving through sensitive attribute-based grouping. In Proc. the 2017 IEEE International Conference on Communications, May 2017, Article No. 792.
    [28]
    Dwork C, McSherry F, Nissim K, Smith A D. Calibrating noise to sensitivity in private data analysis. In Proc. the 3rd Theory of Cryptography Conference, March 2006, pp.265- 284.
    [29]
    Du X, Guizani M, Xiao Y, Chen H. Secure and efficient time synchronization in heterogeneous sensor networks. IEEE Transactions on Vehicular Technology, 2008, 57(4): 2387- 2394.
    [30]
    Aghasian E, Garg S, Gao L, Yu S, Montgomery J. Scoring users’ privacy disclosure across multiple online social networks. IEEE Access, 2017, 5: 13118-13130.
    [31]
    Wasserman L, Zhou S. A statistical framework for differential privacy. Journal of the American Statistical Association, 2010, 105(489): 375-389.
    [32]
    Jorgensen Z, Yu T, Cormode G. Conservative or liberal? Personalized differential privacy. In Proc. the 31st IEEE International Conference on Data Engineering, April 2015, pp.1023-1034.
    [33]
    Wang S, Huang L, Tian M, Yang W, Xu H, Guo H. Personalized privacy-preserving data aggregation for histogram estimation. In Proc. the 2015 IEEE Global Communications Conference, December 2015, Article No. 423.
    [34]
    He Z, Cai Z, Yu J. Latent-data privacy preserving with customized data utility for social network data. IEEE Transactions on Vehicular Technology, 2018, 67(1): 665-673.
    [35]
    Nie Y, Yang W, Huang L, Xie X, Zhao Z, Wang S. A utilityoptimized framework for personalized private histogram estimation. IEEE Transactions on Knowledge and Data Engineering. doi: 10.1109/TKDE.2018.2841360.
    [36]
    McAuley J, Leskovec J. Social circles: Google+. https://snap.stanford.edu/data/egonets-Gplus.html,Nov.2018.
  • Related Articles

    [1]Li-Xian Ma, Le-Ping Wang, En Shao, Rong-Yu Cao, Guang-Ming Tan. VastPipe: A High-Throughput Inference System via Adaptive Space-Division Multiplexing for Diverse Accelerators[J]. Journal of Computer Science and Technology, 2025, 40(2): 444-463. DOI: 10.1007/s11390-024-3773-5
    [2]Chang-Ai Sun, Ming-Jun Xiao, He-Peng Dai, Huai Liu. A Reinforcement Learning Based Approach to Partition Testing[J]. Journal of Computer Science and Technology, 2025, 40(1): 99-118. DOI: 10.1007/s11390-024-2900-7
    [3]Yi-Ge Chen, Yu-Jia Fan, Si-Nan Wang, Yi-Da Tao, Ye-Pang Liu. HmTest: Automated Testing of HarmonyOS Apps via Model-Driven Navigation and Reinforcement Learning[J]. Journal of Computer Science and Technology. DOI: 10.1007/s11390-025-5142-4
    [4]Zhong Qian, Pei-Feng Li, Qiao-Ming Zhu, Guo-Dong Zhou. Document-Level Event Factuality Identification via Reinforced Semantic Learning Network[J]. Journal of Computer Science and Technology, 2024, 39(6): 1248-1268. DOI: 10.1007/s11390-024-2655-1
    [5]Qing-Bin Liu, Shi-Zhu He, Cao Liu, Kang Liu, Jun Zhao. Unsupervised Dialogue State Tracking for End-to-End Task-Oriented Dialogue with a Multi-Span Prediction Network[J]. Journal of Computer Science and Technology, 2023, 38(4): 834-852. DOI: 10.1007/s11390-021-1064-y
    [6]Tong Ding, Ning Liu, Zhong-Min Yan, Lei Liu, Li-Zhen Cui. An Efficient Reinforcement Learning Game Framework for UAV-Enabled Wireless Sensor Network Data Collection[J]. Journal of Computer Science and Technology, 2022, 37(6): 1356-1368. DOI: 10.1007/s11390-022-2419-8
    [7]Tian-Yu Zhao, Man Zeng, Jian-Hua Feng. An Exercise Collection Auto-Assembling Framework with Knowledge Tracing and Reinforcement Learning[J]. Journal of Computer Science and Technology, 2022, 37(5): 1105-1117. DOI: 10.1007/s11390-022-2412-2
    [8]Qing-Bin Liu, Shi-Zhu He, Kang Liu, Sheng-Ping Liu, Jun Zhao. A Unified Shared-Private Network with Denoising for Dialogue State Tracking[J]. Journal of Computer Science and Technology, 2021, 36(6): 1407-1419. DOI: 10.1007/s11390-020-0338-0
    [9]Jia-Ke Ge, Yan-Feng Chai, Yun-Peng Chai. WATuning: A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning[J]. Journal of Computer Science and Technology, 2021, 36(4): 741-761. DOI: 10.1007/s11390-021-1350-8
    [10]Zhi-Feng Xie, Shi Tang, Dong-Jin Huang, You-Dong Ding, Li-Zhuang Ma. Photographic Appearance Enhancement via Detail-based Dictionary Learning[J]. Journal of Computer Science and Technology, 2017, 32(3): 417-429. DOI: 10.1007/s11390-017-1733-z

Catalog

    Article views (216) PDF downloads (622) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return