MaPIR: Mapping-based private information retrieval for location privacy in LBISs

Abstract

The popularization of Location-Based Services (LBSs) has brought along benefits for users and service provider, in terms of improved quality of existing services and a better user experience. At the same time, location privacy has become one of the most critical concerns for ensuring users' right to protection. Despite the fact that one of the best ways to protect the location information is not to reveal it, there are advantages on using this information to personalize services, however it is necessary to guarantee its protection. Private Information Retrieval is a technique that creates a common language between users and service provider so that external actors cannot understand most of the information being transferred. This paper introduces MaPIR, a mapping-based private information retrieval technique that uses mathematically generated mapping to create redundancy in order to provide multiple answers to a user with an undistinguishable location. This technique is decentralized and focuses on Point of Interest Search-based application, not on tracking services. For performance evaluation, were compared in two scenarios MaPIR, a regular spatial query and the Dummy Query technique, the results show that MaPIR takes only half the time of regular geographical queries on database processing, and 5 times less than the Dummy Query technique, while providing a similar level of redundancy.