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Technical Paper

Evaluating Trajectory Privacy in Autonomous Vehicular Communications

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.
Journal Article

Improvement of the Resilience of a Cyber-Physical Remote Diagnostic Communication System against Cyber Attacks

In the near future, vehicles will operate autonomously and communicate with their environment. This communication includes Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) communication, and comunication with cloud-based servers (V2C). To improve the resilience of remote diagnostic communication between a vehicle and external test equipment against cyberattacks, it is imperative to understand and analyze the functionality and vulnerability of each communication system component, including the wired and wireless communication channels. This paper serves as a continuation of the SAE Journal publication on measures to prevent unauthorized access to the in-vehicle E/E system [9], explains the components of a cyber-physical system (CPS) for remote diagnostic communication, analyzes their vulnerability against cyberattacks and explains measures to improve the resiliance.