Towards Privacy-Preserving Spatial Distribution Crowdsensing: A Game Theoretic Approach

Acquiring the spatial distribution of users in mobile crowdsensing (MCS) brings many benefits to users (e.g., avoiding crowded areas during the COVID-19 pandemic). Although the leakage of users' location privacy has received a lot of research attention, existing works still ignore the rationality of users, resulting that users may not obtain satisfactory spatial distribution even if they provide true location information. To solve the problem, we employ game theory with incomplete information to model the interactions among users and seek an equilibrium state through learning approaches of the game. Specifically, we first model the service as a game in the satisfaction form and define the equilibrium for this service. Then, we design a LEFS algorithm for the privacy strategy learning of users when their satisfaction expectations are fixed, and further design LSRE that allows users to have dynamic satisfaction expectations. We theoretically analyze the convergence conditions and characteristics of the proposed algorithms, along with the privacy protection level obtained by our solution. We conduct extensive experiments to show the superiority and various performances of our proposal, which illustrates that our proposal can get more than 85% advantage in terms of the sensing distribution availability compared to the traditional spatial cloaking based solutions.