Measuring Privacy in Vehicular Networks
Isabel Wagner · 2017
Vehicular communication plays a key role in nearfuture automotive transport, promising features like increased traffic safety or wireless software updates. However, vehicular communication can expose driver locations and thus poses important privacy risks. Many schemes have been proposed to protect privacy in vehicular communication, and their effectiveness is usually shown using privacy metrics. However, to the best of our knowledge, (1) different privacy metrics have never been compared to each other, and (2) it is unknown how strong the metrics are. In this paper, we argue that privacy metrics should be monotonic, i.e. that they indicate decreasing privacy for increasing adversary strength, and we evaluate the monotonicity of 32 privacy metrics on real and synthetic traffic with state-ofthe- art adversary models. Our results indicate that most privacy metrics are weak at least in some situations. We therefore recommend to use metrics suites, i.e. combinations of privacy metrics, when evaluating new privacy-enhancing technologies.