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Volume 34 Issue 2
March  2019
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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
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Improving Data Utility Through Game Theory in Personalized Differential Privacy

  • 1 College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China;
    2 School of Information Technology, Deakin University, Melbourne, VIC 3125, Australia;
    3 School of Software, University of Technology Sydney, Sydney, NSW 2007, Australia;
    4 School of Computer Science and Engineering, Beihang University, Beijing 100191, China;
    5 Shanxi Key Laboratory of Advanced Control and Intelligent Information System, Taiyuan University of Science and Technology, Taiyuan 030024, China

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.
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  • 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
    Abstract
  • 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.
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    • References (36)
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