PAPERS: Private and Precise Range Search for Location Based Services

Abstract

Location Based Service (LBS) is gaining popularity on smart phones. One fundamental LBS is range search, which returns all Point of Interests (POIs) within a user-specified range. However, people also leave their location privacy at risks when using LBS like range search. How can a user invoke such service without revealing his location is an interesting, yet challenging problem to solve. Most existing approaches blur a user's location into a cloaked region, so that LBS cannot figure out the exact location of the requesting user. However, this would make the returning results inaccurate, containing some out-of-range POIs. To this end, we propose PAPERS, a new method to provide location privacy for users of range search. PAPERS leverage homomorphic encryption to let the user encrypt her location, and the LBS server can compute distances on ciphertext. In this way, the returning results by LBS are exactly the POIs within the specified range, while LBS learns nothing about user's real location. We implement a prototype of PAPERS, and evaluate it with real POI set of a large-scale production LBS. Experimental results show that PAPERS can achieve the goal of privacy protection, with reasonable overhead in response time and communication cost.