TrafficAdaptor: an adaptive obfuscation strategy for vehicle location privacy against traffic flow aware attacks

One of the most popular location privacy-preserving mechanisms applied in location-based services (LBS) is location obfuscation, where mobile users are allowed to report obfuscated locations instead of their real locations to services. Many existing obfuscation approaches consider mobile users that can move freely over a region. However, this is inadequate for protecting the location privacy of vehicles, as their mobility is restricted by external factors, such as road networks and traffic flows. This auxiliary information about external factors helps an attacker to shrink the search range of vehicles' locations, increasing the risk of location exposure. In this paper, we propose a vehicle traffic flow aware attack that leverages public traffic flow information to recover a vehicle's real location from obfuscated location. As a countermeasure, we then develop an adaptive strategy to obfuscate a vehicle's location by a "fake"trajectory that follows a realistic traffic flow. The fake trajectory is designed to not only hide the vehicle's real location but also guarantee the quality of service (QoS) of LBS. Our experimental results demonstrate that 1) the new threat model can accurately track vehicles' real locations, which have been obfuscated by two state-of-the-art algorithms, and 2) the proposed obfuscation method can effectively protect vehicles' location privacy under the new threat model without compromising QoS.