Preserving Users' Privacy for Location-Based Services

Abstract

The increasing use of location-based services (LBS) poses a significant threat to location privacy of the user. The selection and transmission of dummy locations with the real location in a query has emerged as a common approach to preserve location privacy of a user in LBS. Most of the existing schemes employ entropy maximization as a metric for selecting dummy locations. However, the entropy maximization-based schemes do not preserve privacy in all cases. In this paper, we analyze the limitation of entropy maximization-based schemes in providing privacy. Further, we consider a scenario wherein a stationary user repeatedly uses LBS within a short interval of time and show the adverse impact of the repeated queries of the user on the performance of the existing scheme. We propose an enhanced scheme that preserves privacy of user even for repeated queries. Lastly, numerical results verify the performance of the proposed scheme.