Eunomia: Anonymous and Secure Vehicular Digital Forensics Based on Blockchain

计算机科学 计算机安全 安全性令牌 加密 块链 密码学 互联网隐私
作者
Meng Li,Yifei Chen,Chhagan Lal,Mauro Conti,Mamoun Alazab,Donghui Hu
出处
期刊:IEEE Transactions on Dependable and Secure Computing [IEEE Computer Society]
卷期号:20 (1): 225-241 被引量:37
标识
DOI:10.1109/tdsc.2021.3130583
摘要

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.

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