Abstract. 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.Keywords: VANET, Location Privacy, Tracking, Anonymity IntroductionNowadays, more and more vehicles are equipped with navigation systems that can provide drivers with the directions to the destination. To fulfill their safety functions, many safety-related applications require that the vehicles broadcast their current position, speed, and direction periodically. Because these safety messages are broadcasted wirelessly in plaintext for safety applications, they are vulnerable to eavesdropping, and the location information of the vehicles can then be extracted from either position related data or identification related data. Therefore, although the broadcast safety messages could in principle improve driving safety, unauthorized parties or attackers may exploit the vulnerabilities of these VANET application systems and compromise the location privacy of the interested vehicles [1].To solve the problem mentioned above, the authors in [2][3][4][5] proposed schemes to remove the correlation between locations and identifiers by periodically or randomly updating vehicles' pseudo identifiers. 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