A Framework for Preserving Location Privacy for Continuous Queries

Abstract

The growth of the location-based services (LBSs) market in recent years was motivated by the widespread use of mobile devices equipped with positioning capability and Internet accessibility. To preserve the location privacy of LBS users, many mechanisms have been proposed to provide a partial disclosure by decreasing or blurring or the accuracy of the shared location. While these Location Privacy Preserving Mechanisms (LPPMs) have demonstrated effective performance with snapshot queries, this work shows that preserving location privacy for continuous queries should be addressed differently. In this paper, MOPROPLS framework is proposed with the aim to preserve location privacy in the specific case of continuous queries. As part of the proposed framework, a novel set of six requirements that any LPPM should meet in order to provide location privacy for continuous queries is proposed. In addition, a novel location privacy leakage metric and a novel two-phased probabilistic candidate selection algorithm are proposed. Comparing the performance of MOPROPLS framework with the geo-indistinguishability LPPM in terms of privacy (adversary estimation error) shows that the average of MOPROPLS framework improvement is 34%.