RSSI-based user centric anonymization for location privacy in Vehicular Networks

In Vehicular Networks, for enhancing driving safety as well as supporting other applications, vehicles periodically broadcast safety messages with their precise position information to neighbors. However, these broadcast messages make it easy to track specific vehicles and will likely lead to compromise of personal privacy. Unfortunately, current location privacy enhancement methodologies in VANET, including Pseudonymization, K-anonymity, Random silent period, Mix-zones and path confusion, all suffer some shortcomings. In this paper, we propose a RSSI (Received Signal Strength Indicator)-based user centric anonymization model, which can significantly enhance the location privacy and at the same time ensure traffic safety. Simulations are performed to show the advantages of the proposed method. In comparison with traditional random silent period method, our method can increase at least 47% of anonymity in both simple and correlation tracking.