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Inter-vehicle communication (IVC) is the next step in developing a coordinated vehicular transportation system. With today's concerns of security in Internet communications, it is critical that the security in IVC be considered. With its many life-depending implications it is crucial that an IVC system be tolerant of both natural and man-made interference. The goal of our research was to explore solutions to address the issues of security of IVC in spite of its vulnerability to Denial-of- Service (DoS) attacks, in which an attacker systematically or selectively jams the signals exchanged by the vehicles. We present proposed solutions to DoS attacks on IVC, as well as introduce additional concepts and ideas that help address this important problem.

Inter-vehicle communication is being considered as a means for increasing safety and efficiency in future intelligent highways. However, the security in these future mobile ad hoc networks of vehicles should not be an after thought. The main challenges in developing such security schemes are the highly dynamic environment and the cost restrictions. In this paper, we propose a keyless scheme for message authentication in inter-vehicle communication by verifying the sender’s position and velocity. The approach relies on signal propagation time to authenticate messages being communicated. No infrastructure or dedicated hardware beyond standard GPS is required.

Autonomous vehicles might one day be able to implement privacy preserving driving patterns which humans may find too difficult to implement. In order to measure the difference between location privacy achieved by humans versus location privacy achieved by autonomous vehicles, this paper measures privacy as trajectory anonymity, as opposed to single location privacy or continuous privacy. This paper evaluates how trajectory privacy for randomized driving patterns could be twice as effective for autonomous vehicles using diverted paths compared to Google Map API generated shortest paths. The result shows vehicles mobility patterns could impact trajectory and location privacy. Moreover, the results show that the proposed metric outperforms both K-anonymity and KDT-anonymity.