Bichromatic Reverse Nearest Neighbor Query without Information Leakage

Bichromatic Reverse Nearest Neighbor (BRNN) Query is an important query type in location-based services (LBS) and has many real life applications, such as site selection and resource allocation. However, such query requires the client to disclose sensitive location information to the LBS. The only existing method for privacy-preserving BRNN query adopts the cloaking-region paradigm, which blurs the location into a spatial region. However, the LBS can still deduce some information (albeit not exact) about the location. In this paper, we aim at strong privacy wherein the LBS learns nothing about the query location. To this end, we employ private information retrieval (PIR) technique, which accesses data pages anonymously from a database. Based on PIR, we propose a secure query processing framework together with various indexing and optimization techniques. To the best knowledge, this is the first research that preserves strong location privacy in BRNN query. Extensive experiments under real world and synthetic datasets demonstrate the practicality of our approach.