Eunomia: Anonymous and Secure Vehicular Digital Forensics Based on Blockchain

【Author】 Li, Meng; Chen, Yifei; Lal, Chhagan; Conti, Mauro; Alazab, Mamoun; Hu, Donghui

【Source】IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING

【影响因子】6.791

【Abstract】Vehicular Digital Forensics (VDF) is essential to enable liability cognizance of accidents and fight against crimes. Ensuring the authority to timely gather, analyze, and trace data promotes vehicular investigations. However, adversaries crave the identity of the data provider/user, damage the evidence, violate evidence jurisdiction, and leak evidence. Therefore, protecting privacy and evidence accountability while guaranteeing access control and traceability in VDF is no easy task. To address the above-mentioned issues, we propose Eunomia: an anonymous and secure VDF scheme based on blockchain. It preserves privacy with decentralized anonymous credentials without trusted third parties. Vehicular data and evidence are uploaded by data providers to the blockchain and stored in distributed data storage. Each investigation is modeled as a finite state machine with state transitions being executed by smart contracts. Eunomia achieves fine-grained evidence access control via ciphertext-policy attribute-based encryption and Bulletproofs. A user must hold specific attributes and a temporary-and-unexpired token/warrant to retrieve data from the blockchain. Finally, a secret key is embedded into data to trace the traitor if any evidence breach happens. We use a formal analysis to demonstrate the strong privacy and security properties of Eunomia. Moreover, we build a prototype in a WiFi-based Ethereum test network to evaluate its performance.

【Keywords】Vehicular networks; digital forensics; privacy; security; blockchain

【发表时间】2023 1-Jan

【收录时间】2023-05-02

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-交通领域