Location privacy-protection based on p-destination in mobile social networks: A game theory analysis

Abstract

k-anonymity and l-diversity are widely discussed means of controlling the degree of privacy loss when personal information is processed for data analytics. User privacy can easily be disclosed by tracking its past/future locations. In this paper, we propose a Location Privacy Protection (LPP) method which enables a trusted third party to aggregate location-aware requests based on p-destination in mobile social networks. Our LPP can prevent an attacker from associating users' identities, locations and query contents. We also propose a hide-and-seek game-theoretic model for developing defense strategies for the rational trusted third party in dealing with a rational attacker. Detailed analysis is provided for choosing strategies that maximize payoffs, and simulation results are provided to demonstrate that our proposed method protects user privacy.