AMOEBA: Robust Location Privacy Scheme for VANET

Abstract

Abstract-Communication messages in vehicular ad hoc networks (VANET) can be used to locate and track vehicles. While tracking can be beneficial for vehicle navigation, it can also lead to threats on location privacy of vehicle user. In this paper, we address the problem of mitigating unauthorized tracking of vehicles based on their broadcast communications, to enhance the user location privacy in VANET. Compared to other mobile networks, VANET exhibits unique characteristics in terms of vehicular mobility constraints, application requirements such as a safety message broadcast period, and vehicular network connectivity. Based on the observed characteristics, we propose a scheme called AMOEBA, that provides location privacy by utilizing the group navigation of vehicles. By simulating vehicular mobility in freeways and streets, the performance of the proposed scheme is evaluated under VANET application constraints and two passive adversary models. We make use of vehicular groups for anonymous access to location based service applications in VANET, for user privacy protection. The robustness of the user privacy provided is considered under various attacks.

RSSI-Based User Centric Anonymization for Location Privacy in Vehicular Networks

“…Then we use both simple and correlation tracking methods [7] to measure the anonymity of the tracked vehicles. The anonymity of a vehicle is determined by its anonymity set, where the latter is defined as vehicles locating within the reachable area of the tracked vehicles, but is indistinguishable from each other.…”

“…In addition to random silent period, Sampigethaya [7,21] also proposes a group communication scheme in VANET. The scheme in [15] can also enhance privacy by using group communication.…”

“…By altering broadcast interval, random silent period (RSP) becomes a useful method to enhance location privacy [6,7,20]. However, when the vehicles are close to each other, indiscriminately following random silent period rules may result in vehicle collision.…”

“…Entropy of the vehicle distribution in the anonymity set is the most popular method to evaluate the level of anonymity [7,14]. Equation (9) is used to measure the entropy.…”

“…Although these methods could make vehicles unidentifiable within an anonymity set in motionless states, it can still be traced by movement tracking [6]. Furthermore, the locations visited by the vehicles can be associated with the places of interests [7] by firstly accumulating the driving paths, then by cross-referencing the accumulation results with geographical maps or other location based services. Existing methodologies to enhance location privacy can be classified into several categories: k-anonymity [8][9][10], path confusion [11,12], and Mix-Zones [13,14].…”