Cognitive Approach for Location Privacy Protection

While enjoying the convenience of location-based services (LBSs) in everyday life, wireless device users could also put their location privacy at risk. An untrusted LBS provider can store mobile users' data on its server, track users in various ways or share users location data to the third parties. To protect LBS users' privacy, many position confusion algorithms were proposed, but those algorithms often have difficulty balancing the utility-privacy tradeoffs. In this paper, we propose a new cognitive approach that enables nearcomplete privacy protection for LBS users by leveraging existing social network resources. We introduce a heterogeneous multi-server architecture that cuts off the direct connection between the LBS queries and the query issuers, and an auction-based incentive mechanism guaranteed user participation, which is critical for the success of the proposed architecture. A simulation system and a smartphone application were developed, and our evaluation results show that the proposed method can not only achieve the near-total privacy protection for LBS users, but also significantly improve the quality of the services.