Towards Privacy-Preserving Vehicle Digital Forensics: A Blockchain Approach

Abstract

Vehicle digital forensics (VDF), encompasses the investigation of digital evidence related to vehicles, plays a crucial role in modern transportation systems, aiding in accident investigations, crime detection, and ensuring road safety. However, the need to collect data for such investigations has exacerbated privacy concerns, as sensitive vehicular data is susceptible to unauthorized access and exploitation. While blockchain technology has been explored in the literature to address these challenges, existing techniques often prioritize user anonymity over data unlinkability, limiting their effectiveness in preserving privacy. In response, this paper proposes a novel blockchain-based networking strategy for VDF, aiming to achieve both user anonymity and data unlinkability concurrently. By lever-aging group signatures and secure communication protocols, the proposed strategy ensures the integrity of vehicular data while mitigating privacy risks. Performance evaluations demonstrate the efficacy of the strategy in terms of computation and communication overheads, while comparative analyses highlight its superiority over existing approaches in terms of privacy preservation and security.