A personalized range‐sensitive privacy‐preserving scheme in LBSs

Mobile social network has become a necessary part in our daily life, and location-based services (LBSs) provide unprecedented convenience to mobile users. However, these attracting services are accompanied with privacy disclosures, including location privacy and query privacy. Mobile users have to expose their personal information to untrusted location-based service provider (LSP) in order to obtain relevant service data. To address these privacy issues, we proposed a personalized range-sensitive privacy-preserving scheme, called PRPS, which considers the relationship between locations, query ranges, and query contents. Moreover, PRPS employs map storing algorithm (MSA) to facilitate the storage of two-dimensional local map, reducing the cost of storage. Furthermore, range estimating algorithm (REA) adopts binary quad-tree to decide the query radius of each submitted location, avoiding inference attacks by adversary. The requirements generating algorithm (RGA) selects relevant query content for each dummy location, guaranteeing mobile user's location privacy and query privacy. Finally, we illustrate the privacy analysis to proof PRPS's privacy degree; then, the performance and privacy evaluation results indicate that the proposed PRPS is effective and efficient.