Near-Complete Privacy Protection: Cognitive Optimal Strategy in Location-Based Services

While enjoying the amenities and convenience provided by the location-based service (LBS) in our daily life, wireless device users may surrender their location together with social activity privacy to the LBS provider. The untrusted LBS server has all the information about users in the LBS and it may track them in various ways or release their privacy data to third parties. To address the privacy protection issue, many location obfuscation algorithms behind a location-privacy preserving mechanisms (LPPMs) were proposed but only approached the trade-off between utility and privacy. Unlike the existing approaches, we propose the first methodology and application, to the best of our knowledge, that enables a near-complete location privacy protection for LBS users to achieve an cognitive optimal resolution by employing the social network associated with the LBS to separate the utility and privacy. Evaluation of both simulation and practical smartphone application shows that the proposed methodology could achieve a near-complete privacy protection in terms of entropy and significantly improve the quality of service utility.