Anonymous, Secure, Traceable, and Efficient Decentralized Digital Forensics

Journal article (2024)

Authors

Meng Li Hefei University of Technology
Yanzhe Shen Huazhong University of Science and Technology
Guixin Ye Northwest University China
Jialing He Chongqing University
Xin Zheng The Anhui Provincial Department of Justice
Zijian Zhang Beijing Institute of Technology
Liehuang Zhu University of Padua
M. Conti Cyber Security - EEMCS
Research Group
Cyber Security (EEMCS) (TU Delft)
Blockchain Security Privacy Access control Efficiency Decentralized digital forensics SGX2 Watermark
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Published Date

2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Cyber Security

Abstract

Digital forensics is crucial to fight crimes around the world. Decentralized Digital Forensics (DDF) promotes it to another level by channeling the power of blockchain into digital investigations. In this work, we focus on the privacy and security of DDF. Our motivations arise from (1) how to track an anonymous- and-malicious data user who leaks only a part of the previously requested data, (2) how to achieve access control while protecting data from untrusted data centers, and (3) how to enable efficient and secure search on the blockchain. To address these issues, we propose Themis: an anonymous and secure DDF scheme with traceable anonymity, private access control, and efficient search. Our framework is boosted by establishing a Trusted Execution Environment in each authority (blockchain node) for securing the uploading, requesting, and searching. To instantiate the framework, we design a secure and robust watermarking scheme in conjunction with decentralized anonymous authentication, a private and fine-grained access control scheme, and an efficient and secure search scheme based on a dynamically updated data structure. We formally define and prove the privacy and security of Themis. We build a prototype with Ethereum and Intel SGX2 to evaluate its performance, which supports processing data from a considerable number of data providers and investigators.