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Improving Data Utility Through Game Theory in Personalized Differential Privacy

Journal of Computer Science & Technology 2019年第2期34卷 272-286页

作者： Lei Cui Youyang Qu mohammad reza nosouhi Shui Yu Jian-Wei Niu Gang Xie College of Information and Computer Taiyuan University of Technology Taiyuan 030024 China School of Information Technology Deakin University Melbourne VIC 3125 Australia School of Software University of Technology Sydney Sydney NSW 2007 Australia School of Computer Science and Engineering Beihang University Beijing 100191 China Shanxi Key Laboratory of Advanced Control and Intelligent Information System Taiyuan University of Science and Technology Taiyuan 030024 China

Due to dramatically increasing information published in social networks,privacy issues have given rise to public *** the presence of differential privacy provides privacy protection with theoretical foundations,the trade-off between privacy and data utility still demands further ***,most existing studies do not consider the quantitative impact of the adversary when measuring data *** this paper,we firstly propose a personalized differential privacy method based on social ***,we analyze the maximum data utility when users and adversaries are blind to the strategy sets of each *** formalize all the payoff functions in the differential privacy sense,which is followed by the establishment of a static Bayesian *** trade-off is calculated by deriving the Bayesian Nash equilibrium with a modified reinforcement learning *** proposed method achieves fast convergence by reducing the cardinality from n to *** 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 *** extensive experiments on the real-world dataset prove the proposed model is effective and feasible.

关键词： personalized privacy protection game theory trade-off reinforcement learning

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