Game theoretic approach to location sharing with privacy in a community-based mobile safety application

A new generation of community-based social networking mobile applications is emerging. In these applications, there is often a fundamental tension between users' desire for preserving the privacy of their own data and their need for fine-grained information about others. Our work is motivated by a community-based mobile application called Aegis, a personal safety enhancement service based on sharing location information with trusted nearby friends. We model the privacy-participation tradeoffs in this application using a game theoretic formulation. Users in this game are assumed to be self-interested. They prefer to obtain more fine-grained knowledge from others while limiting their own privacy leak (i.e. their own contributions to the game) as much as possible. We design a tit-for-tat mechanism to give user incentives to contribute to the application. We investigate the convergence of two best response dynamics to achieve a non-trivial Nash equilibrium for this game. Further, we propose an algorithm that yields a Pareto optimal Nash equilibrium. We show that this algorithm guarantees polynomial time convergence and can be executed in a distributed manner.