Spatiotemporal correlation-aware dummy-based privacy protection scheme for location-based services

Abstract

Since the dummy-based method can provide precise query results without any requirement for a third party or key sharing, it has been widely used to protect the user's location privacy in location-based services. However, the neighboring location sets submitted in consecutive requests always include a close spatiotemporal correlation, which enables the adversary to identify some dummies. Therefore, the existing dummy-based schemes cannot protect the user's location privacy completely. To solve this problem, based on the dummies generated by the existing schemes, this paper filters out the dummies that can be identified by taking into account of the spatiotemporal correlation from three aspects, namely time reachability, direction similarity and in-degree/out-degree. In this way, the rest dummies can satisfy the user's personalized privacy protection requirement. Security analysis shows that the proposed scheme successfully perturbs the spatiotemporal correlation between neighboring location sets, therefore, it is infeasible for the adversary to distinguish the user's real location from the dummies. Furthermore, extensive experiments indicate that the proposal is able to protect the user's location privacy effectively and efficiently.