Practical k nearest neighbor queries with location privacy

In mobile communication, spatial queries pose a serious threat to user location privacy because the location of a query may reveal sensitive information about the mobile user. In this paper, we study k nearest neighbor (kNN) queries where the mobile user queries the location-based service (LBS) provider about k nearest points of interest (POIs) on the basis of his current location. We propose a solution for the mobile user to preserve his location privacy in kNN queries. The proposed solution is built on the Paillier public-key cryptosystem and can provide both location privacy and data privacy. In particular, our solution allows the mobile user to retrieve one type of POIs, for example, k nearest car parks, without revealing to the LBS provider what type of points is retrieved. For a cloaking region with n×n cells and m types of points, the total communication complexity for the mobile user to retrieve a type of k nearest POIs is O(n+m) while the computation complexities of the mobile user and the LBS provider are O(n + m) and O(n^2m), respectively. Compared with existing solutions for kNN queries with location privacy, our solutions are more efficient. Experiments have shown that our solutions are practical for kNN queries