Privacy-aware Methods for Data Sharing between Autonomous Vehicles*

Abstract

Connected autonomous vehicles (CAVs) can share raw data directly about traffic, and road conditions to support real-time decision-making. Despite the potential for optimizing the flow of traffic, data sharing also raises privacy concerns as the location and timeliness of the reported events might reveal the routes of the vehicles. Therefore, a vehicle shall be careful when sharing even a part of its measurement data. In this paper, several data selection methods are proposed to decide how much data to share. The selection methods are also evaluated in terms of the utility and privacy sensitivity of the shared data.Simulations based on a Markov chain model indicate that a greedy method may provide the most information while it does not reveal more than other feasible approaches.