Demo Data from EDBT 2026 / reviewers in the wild / expert
Yanzhe Shen
dblp:373/0025
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0007-0659-2333ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Digital forensics and information hiding · 30% Blockchain and cryptocurrency security · 30% Privacy and data protection · 30% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Blockchain and cryptocurrency security
blockchain-based access control |
0.8 | 1 | 2024 | Anonymous, Secure, Traceable, and Efficient Decentralized Digital Forensics · IEEE Trans. Knowl. Data Eng. 2024 |
Privacy and data protection › anonymity
traceable anonymity |
0.8 | 1 | 2024 | Anonymous, Secure, Traceable, and Efficient Decentralized Digital Forensics · IEEE Trans. Knowl. Data Eng. 2024 |
Hardware security and side channels
trusted execution environments |
0.2 | 1 | 2024 | Anonymous, Secure, Traceable, and Efficient Decentralized Digital Forensics · IEEE Trans. Knowl. Data Eng. 2024 |
Methods — techniques the papers use, named apart from their topics
watermarking · 0.8secure search · 0.8anonymous authentication · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Anonymous, Secure, Traceable, and Efficient Decentralized Digital ForensicsabstractDigital 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. Meng Li 0006, Yanzhe Shen, Guixin Ye, Jialing He, Zijian Zhang 0001, Liehuang Zhu, Mauro Conti |
IEEE Trans. Knowl. Data Eng. | 2 |