Peer-to-Peer Misbehavior Reporting Using Non-Interactive Zero-Knowledge Proofs for Intelligent Transport Systems

Abstract

The integration of technologies like the Internet of Things (IoT), Big data, and Artificial Intelligence (AI) has empowered modern vehicles with the ability to communicate with one another for better cooperation on the roads. However, the communication between vehicles exposes the whole intelligent transportation system to new attack vectors. Malicious vehicles can spread misleading information, which, if acted upon, might result in traffic congestion, accidents, chaos, and even fatalities. As a countermeasure, the European Telecommunications Standards Institute (ETSI) proposes a framework, TR 103 460, for reporting abnormal behavior. However, there are many shortcomings, such as the absence of a peer-to-peer (P2P) misbehavior reporting (MR) service and the inability to secure the reporter's identity and reported information. To protect vehicles from abuse, we propose a P2P non-interactive zero-knowledge proof-based privacy-preserving MR framework. Upon testing, we found that the proposed framework prevented the disclosure of the reporter's identity and information and reduced the ITS-Stations' (ITS-Ss) exposure to misbehavior by 67.7% and 79.2% in suburban and highway traffic scenarios, respectively.