Secure Data Provenance in Internet of Vehicles with Verifiable Credentials for Security and Privacy

Abstract

The emergence of the Internet of Vehicles (IoVs) has also exposed security challenges that require advanced strategies to maintain secure data provenance (SDP) and ensure data credibility and anomaly detection. This paper introduces an innovative framework tailored for the dynamic and distributed nature of IoVs that enables the secure tracing of data origins and ensures the reliability of data through plausibility checks. Our approach leverages the principles of decentralized SDP using Verifiable Credentials (VCs) and distributed ledger technology (DLT) to establish a traceable and tamper-evident data lineage, enhancing the integrity and authenticity of vehicular communications. To address the complexities of anomaly detection, we integrate checks that scrutinize data streams for abnormal patterns, enabling the timely identification and mitigation of potential security breaches. We also propose a robust mechanism to assess data plausibility, ensuring that only credible and verifiable data influence the decision-making processes in the IoVs ecosystem. Through detailed experimentation and analysis, our methodology demonstrates significant improvements in securing IoVs against common threats such as impersonation, data tampering, and privacy breaches. This fosters a trustworthy and resilient vehicular network environment.