Hiding in the Mobile Crowd: Location Privacy through Collaboration
Location-aware smartphones support various location-based services (LBSs): users query the LBS server and learn on the fly about their surroundings. However, such queries give away private information, enabling the LBS to track users. A user-collaborative privacy-preserving approach is proposed for LBSs. This solution does not require changing the LBS server architecture and does not assume third party servers; yet, it significantly improves usersÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ location privacy. The gain stems from the collaboration of mobile devices: they keep their context information in a buffer and pass it to others seeking such information. Thus, a user remains hidden from the server, unless all the collaborative peers in the vicinity lack the sought information. A novel epidemic model is developed to capture possibly time-dependent, dynamics of information propagation among users. Used in the Bayesian inference framework, this model helps analyze the effects of various parameters, such as usersÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ querying rates and the lifetime of context information, on usersÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ location privacy. The results show that our scheme hides a high fraction of location-based queries, thus significantly enhancing usersÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ location privacy. Finally, implementation indicates that it is lightweight and the cost of collaboration is negligible.