Privacy-Preserving Spatial Queries in Location Based Services

Speaker: Gabriel Ghinita

Abstract

The emerging trend of mobile devices with embedded positioning capabilities (e.g., GPS) facilitates the widespread use of Location Based Services. For such applications to succeed, query privacy and confidentiality are of paramount importance. Conventional privacy-preserving techniques include encryption, which safeguards communication channels, and the use of pseudonyms, which hide user identities. Nevertheless, the contents of spatial queries may disclose the physical location and identity of users, compromising their privacy. We present a framework for preserving the privacy of users who issue spatial queries to Location Based Services. We propose transformations based on the well-established k-anonymity paradigm to compute exact answers for Range and Nearest Neighbor queries, without revealing the query source identity. Our proposed techniques provide guarantees on user privacy, and can be employed both in the centralized setting, as well as in decentralized/P2P systems. Extensive experimental studies show that our methods are applicable to real-life scenarios with numerous mobile users.