Improving Data Utility Through Game Theory in Personalized Differential Privacy
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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.
Keywordspersonalized privacy protection game theory trade-off reinforcement learning
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- 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.Google Scholar
- 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.Google Scholar
- 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, May 2018.Google Scholar
- Machanavajjhala A, Kifer D, Gehrke J, Venkitasubra-Maniam M. L-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data, 2007, 1(1): Article No. 3.Google Scholar
- 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.Google Scholar
- Dwork C. Differential privacy. In Proc. the 33rd International Colloquium on Automata, Languages and Programming, July 2006, pp.1-12.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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: https://doi.org/10.1109/TETC.2017.2754646.
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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: https://doi.org/10.1109/TKDE.2018.2841360.
- McAuley J, Leskovec J. Social circles: Google+. https://snap.stanford.edu/data/egonets-Gplus.html, Nov. 2018.