A Differential Game Model for Data Utility and Privacy-Preserving in Mobile Crowdsensing

Mobile crowdsensing (MCS) is becoming an extremely pervasive sensing paradigm with the popularization of intelligent devices, which needs users to release their data to the sensing platform. But to the MCS system, user's privacy-preserving demands may be time-varying in the data releasing process. In addition, protecting data privacy and ensuring data utility is becoming a contradictory and critical issue, which results in a trade-off problem that needs to be solved. In this article, we construct a differential game model to solve the trade-off problem between the data utility and privacy preserving in mobile crowdsensing system, and solve the feedback Nash equilibrium solutions based on the dynamic programming in the MCS system. Based on the feedback Nash equilibrium solutions, users and the platform can achieve maximization of privacy requirement and data utility, respectively. Ultimately, a numerical simulation has been made to show the correctness of the proposed differential game model.