Eunomia: Anonymous and Secure Vehicular Digital Forensics based on Blockchain

Meng Li, Yifei Chen, Chhagan Lal, Mauro Conti, Mamoun Alazab, Donghui Hu

    Research output: Contribution to journalArticlepeer-review

    27 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)225-241
    Number of pages17
    JournalIEEE Transactions on Dependable and Secure Computing
    Volume20
    Issue number1
    Early online dateNov 2021
    DOIs
    Publication statusPublished - 1 Jan 2023

    Bibliographical note

    Funding Information:
    This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 62002094, the Anhui Provincial Natural Science Foundation under Grant 2008085MF196, Anhui Science and Technology Key Special Program under Grant 201903a05020016, and the National Natural Science Foundation of China (NSFC) under Grant U1836102. It is partially supported by EU LOCARD Project under Grant H2020-SU-SEC-2018-832735.

    Publisher Copyright:
    © 2004-2012 IEEE.

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