On the Strength of Privacy Metrics for Vehicular Communication

Abstract

Vehicular communication plays a key role in near-future automotive transport, promising features such as increased traffic safety and wireless software updates. However, vehicular communication can expose drivers' locations and thus poses privacy risks. Many schemes have been proposed to protect privacy in vehicular communication, and their effectiveness is usually evaluated with 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 evaluate and compare the strength of 41 privacy metrics in terms of four novel criteria: Privacy metrics should be monotonic, i.e., indicate decreasing privacy for increasing adversary strength; their values should be spread evenly over a large value range to support within-scenario comparability; and they should share a large portion of their value range between traffic conditions to support between-scenario comparability. We evaluate all four criteria on real and synthetic traffic with state-of-the-art adversary models and create a ranking of privacy metrics. Our results indicate that no single metric dominates across all criteria and traffic conditions. We therefore recommend to use metrics suites, i.e., combinations of privacy metrics, when evaluating new privacy-enhancing technologies.