Perturbation-Hidden: Enhancement of Vehicular Privacy for Location-Based Services in Internet of Vehicles

Abstract

The great development of smart networks enables Internet of Vehicles (IoV) as a promising paradigm to provide pervasive services, where privacy issues for location-based services (LBSs) have attracted considerable attention. In terms of location privacy, inspired by differential privacy, geo-indistinguishability (Geo-Ind) has recently become a prevalent privacy model for LBSs. Although Geo-Ind guarantees the location privacy, users’ other privacy concerns are still at risk if the location perturbation behavior is exposed due to implausible reported locations. Through experiments we find the probability that the classical Geo-Ind mechanism perturbs the true location to implausible areas can be more than 50%. To address it, we first propose an enhanced privacy definition beyond Geo-Ind, called Perturbation-Hidden, to prevent location perturbation behaviors of users from being recognized by guaranteeing their pseudo-locations plausible. Then we design a mechanism to achieve this definition by transplanting the differential private exponential mechanism to our approach. Furthermore, we propose a method for determining the retrieval area utilizing dynamic programming to ensure the accuracy of LBSs. Finally, we theoretically prove that our mechanism satisfies the privacy definition. Extensive experiments on simulations and a real-world dataset show that our proposal achieves 100% plausible pseudo-locations while ensuring high query precision and recall.