Privacy Preserving Reverse k-Nearest Neighbor Queries

Abstract

Reverse k-nearest neighbor (RkNN) queries are prevalent in location-based services to find those locations that have the query point as one of their k nearest neighbors. However, such query requires users to disclose the location of the query point to a service provider who might be untrustworthy. Previous attempts to preserve the privacy of RkNN queries are either based on weaker notions of privacy such as location cloaking or not efficient when k > 1. In this paper, we propose novel solutions based on the private information retrieval (PIR) mechanism to preserve the privacy of RkNN query points. Our solutions include server-side data indexing and client-side query processing methods to facilitate PIR which is an inherently expensive data retrieval mechanism. We experimentally evaluate our approach using real-world datasets and show that it preserves the location privacy of queries with reasonable computation and storage overhead.