Abstract: Mobile crowdsensing system should offer the personalized privacy protection of users’ location to attract more users to participate in the task.However,due to the existence of malicious attackers,users’ enhanced privacy protection will lead to poor location availability and reduce the efficiency of task allocation.To solve this problem,this paper proposes a win-win game based on reinforcement learning.Firstly,two virtual entities of the trusted third party are used to simulate the interaction between users and the platform,one simulating user chooses the privacy budget to add noise to their locations and the other simulates the platform allocating tasks with users’ disturbed locations.Then,the interaction process is constructed as a game,in which the two virtual entities of interaction are the adversaries,and the equilibrium point is derived.Finally,the reinforcement learning method is used to try different location disturbance strategies and output an optimal location disturbance scheme.The experimental results show that the mechanism can optimize the task distribution utility while improving the user’s overall utility as much as possible,so that the user and the platform can achieve a win-win situation.

Key words: Game theory, Mobile crowdsensing, Personalized privacy-preserving, Reinforcement learning, Task allocation