Privacy-Preserving Data-Prefetching in Vehicular Networks via Reinforcement Learning

Abstract

Prefetching and caching content at road-side units (RSUs) and broadcast-transmission scheduling can be shown to improve the throughput in vehicular networks considerably, provided vehicular trajectories prediction are accurate enough. As such many studies assume implicitly that such trajectories are accurately available via GPS or location tracking. In practice, as discussed in the standards, this raises a big issue of privacy. In this paper, we focus on jointly optimizing the broadcastscheduling throughput from RSUs to vehicles, while preserving the privacy of vehicles by enabling them to disseminate obfuscated location information to the server. As it is difficult to predict the vehicular throughput according to its disseminated obfuscated locations, we propose to use the capacity based on reported information as an approximation. We formulate the problem as a reinforcement learning (RL) problem, where the decision variables concern the action to obfuscate disseminated location according to current location and the last disseminated location, with the objective of maximizing a utility function that consists of a weighted sum of the capacity and the level of privacy. Simulation results show that, the proposed scheme consistently outperforms the randomized benchmark, and is insensitive to the prediction accuracy of vehicles' future locations.