K-DLCA: An efficient approach for location privacy preservation in location-based services

Abstract

Location-Based Service (LBS) is one of the fundamental and central functionalities of mobile social networks. Since users usually have to report their locations to the LBS providers while using services, the protection of user's location privacy poses a critical challenge. Although many existing approaches can preserve user's location privacy effectively, most of them must include and use the user's real location. In this paper, we first propose an efficient k-anonymity based Dummy Location and divided Circular Area (k-DLCA) approach to protect the user's location privacy. Different from existing studies, the k-DLCA algorithm adopts a greedy strategy to select dummy locations and considers the semantic location information of the location. Moreover, the user's real location may not be contained in the chosen dummy locations. We then show that k-DLCA algorithm can resist the attacks from adversaries, and has a low probability of exposing the user's real location. We conduct extensive simulations to evaluate the efficiency of the proposed scheme. The simulation results demonstrate that our proposed scheme is promising.