EDBT 2026 Demo / reviewers in the wild / expert
Zilin Liu
dblp:313/8139
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Computer networks · 1 · 1 since 2021Security and privacy · 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 |
Security and privacy of machine learning · 67% Privacy and data protection · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Security and privacy of machine learning › federated learning defense
byzantine-robust federated learning |
1.0 | 1 | 2026 | Publicly Auditable Federated Learning With Privacy and Byzantine Robustness · IEEE Trans. Dependable Secur. Comput. 2026 |
Security and privacy of machine learning
federated learning security |
1.0 | 1 | 2026 | Publicly Auditable Federated Learning With Privacy and Byzantine Robustness · IEEE Trans. Dependable Secur. Comput. 2026 |
Privacy and data protection
privacy-preserving machine learning |
1.0 | 1 | 2026 | Publicly Auditable Federated Learning With Privacy and Byzantine Robustness · IEEE Trans. Dependable Secur. Comput. 2026 |
Methods — techniques the papers use, named apart from their topics
secure aggregation · 1.0byzantine robustness · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | WHERE: Weekly hypergraph evolving representation learning for next POI recommendation
Zilin Liu, Botao Jiang |
Neurocomputing | 1 |
| 2026 | Publicly Auditable Federated Learning With Privacy and Byzantine Robustness
Huang Zeng, Anjia Yang, Jian Weng 0001, Min-Rong Chen, Fengjun Xiao, Zilin Liu, Yi Liu 0053 |
IEEE Trans. Dependable Secur. Comput. | 6 |
| 2024 | PACDAM: Privacy-Preserving and Adaptive Cross-Chain Digital Asset MarketplaceabstractAs the deployment of blockchains expands across various industries, the demand for exchanging digital assets among blockchain users has risen. Most of existing solutions either solely support asset exchanges among users on the same blockchain, or have limitations by only enabling cross-chain asset exchanges among a few specific blockchains or requiring an intermediary to involve in the cross-chain transaction. To address this problem, in this paper, we propose the concept of cross-chain digital asset marketplace which enables users across different blockchains to exchange their assets securely and efficiently. We then propose a privacy-preserving and adaptive cross-chain digital asset marketplace scheme, denoted as PACDAM. It adaptively matches purchasers’ requests and ensures atomic and privacy-preserving cross-chain transactions. Built on adaptor signatures and randomizable time-lock puzzles, the cross-chain transaction procedure only relies on the underlying blockchain for signature verification, making PACDAM compatible with various blockchains. Furthermore, this protocol eliminates the necessity for third-party involvement (e.g., brokers) in cross-chain transactions, leading to a substantial enhancement in system efficiency and scalability. We also give a comprehensive security analysis of PACDAM, demonstrating its robustness against common attacks and preserving the privacy of transaction participants. Finally, we conduct a series of experiments, and the results validate the effectiveness of our proposed scheme. Jia-Nan Liu, Anjia Yang, Jian Weng 0001, Min-Rong Chen, Zilin Liu, Ming Li 0049 |
IEEE Internet Things J. | 6 |