ATPS: An AI Based Trust-Aware and Privacy-Preserving System for Vehicle Managements in Sustainable VANETs

Abstract

Vehicular Ad hoc Networks (VANETs), as the integration of mobile vehicle and intelligent technology, has raised plenty of attentions. In VANETs, mobile smart vehicles can timely get information from surrounding environment, which brings great convenience for society. However, this highlights the need to improve data quality while protecting privacy for vehicular data provider. Thus, in this article, we propose an AI-based Trust-aware and Privacy-preserving System (ATPS) to preserve privacy for vehicular data providers while improving quality of data collections in VANETs. Our proposed ATPS system mainly consists of two schemes: 1) a Partial Ordering based Trust Management (POTM) scheme and 2) a Trajectory Privacy Preserving (TPP) scheme jointly designed by Wasserstein Generative Adversarial Networks (WGAN) and differential privacy. The POTM scheme uses partial ordering relationship to accurately evaluate and manage trusts for vehicular data providers with the assistance of fully trusted drones, and selects the vehicles with top ranks to collect data. Then, TPP scheme skillfully incorporates WGAN and differential privacy to preserve trajectory privacy for vehicular data providers in VANETs, which also guarantees data availability by adding carefully designed noise to the original trajectory. Compared to existing scheme, extensive experiments conducted on the real-world datasets demonstrates efficiency of our ATPS in terms of improving the data quality by 45.76% to 52.57%, reducing the malicious vehicle participants by 15.48% to 16.95%, preserving privacy of vehicles, and guaranteeing data availability.