Demo Data from EDBT 2026 / reviewers in the wild / expert
Bingren Chen
dblp:231/9048
· DBLP profile ↗
4ranked-venue papers
0as first author
3since 2021 · last 2026
0000-0001-8297-5108ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 2 since 2021Databases, data management, data science and information retrieval · 1Theory of computation · 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
2 papers |
Blockchain and cryptocurrency security · 75% Privacy and data protection · 25% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Distributed systems · 100% | |
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Blockchain and cryptocurrency security
consensus protocol |
0.7 | 1 | 2023 | Avalon: A Scalable and Secure Distributed Transaction Ledger Based on Proof-of-Market · IEEE Trans. Computers 2023 |
Blockchain and cryptocurrency security › consensus protocol
proof-of-work |
0.7 | 1 | 2023 | Avalon: A Scalable and Secure Distributed Transaction Ledger Based on Proof-of-Market · IEEE Trans. Computers 2023 |
Distributed systems › blockchain
blockchain scalability |
0.7 | 1 | 2023 | Avalon: A Scalable and Secure Distributed Transaction Ledger Based on Proof-of-Market · IEEE Trans. Computers 2023 |
Distributed systems › distributed database
distributed ledger |
0.7 | 1 | 2023 | Avalon: A Scalable and Secure Distributed Transaction Ledger Based on Proof-of-Market · IEEE Trans. Computers 2023 |
Privacy and data protection › privacy-preserving machine learning
privacy-preserving neural network training |
0.4 | 1 | 2020 | TransNet: Training Privacy-Preserving Neural Network over Transformed Layer · Proc. VLDB Endow. 2020 |
Algorithmic game theory and mechanism design › mechanism design
incentive compatibility |
0.2 | 1 | 2023 | Avalon: A Scalable and Secure Distributed Transaction Ledger Based on Proof-of-Market · IEEE Trans. Computers 2023 |
Methods — techniques the papers use, named apart from their topics
proof-of-work · 2.0matching incentive · 2.0market-driven leader election · 2.0privacy-preserving training · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Efficient quantum detectable weak Byzantine agreement with optimal fault-tolerant boundabstractQuantum entanglement can be employed in some distributed communication tasks to reduce computation and communication overheads, yielding speedups over their classical counterparts. However, existing quantum Byzantine protocols often entail excessive communication and entanglement resource consumption. Conversely, classical schemes face insecurity with the impending reality of quantum computers. To address these challenges, we present a novel quantum Detectable Weak Byzantine Agreement (DWBA) protocol with high practicability, security and optimal fault-tolerance bound. For all n players, our protocol can complete the consensus of infinite classical information via fixed n + 1 -entangled qubits and a digest function, which requires infinite entanglement resources in previous schemes. Moreover, our protocol can tolerate arbitrary t ( t < n ) faulty players without necessitating any additional initial setup information (e.g., digital signature, private random coins). In terms of efficiency, our protocol only requires O (1) rounds and O ( ( n − t ) 2 ) communication complexity, constituting an order of magnitude improvement over previous protocols. In addition, the DWBA problem solved by our protocol is inherently stronger than the 3-player Detectable Byzantine Agreement (DBA) problem solved by previous protocols, and in a weaker but sufficiently practical model, our protocol can achieve Weak Byzantine Agreement (WBA). Lide Xue, Wei Yang 0011, Bingren Chen, Weilin Chen 0002, Liusheng Huang |
Inf. Comput. | 3 |
| 2024 | AutoMiner: Reinforcement Learning-Based Mining Attack Simulator
Lide Xue, Ziyang Han, Bingren Chen, Xishan Zhang, Xuehai Zhou |
ICA3PP (1) | 4 |
| 2023 | Avalon: A Scalable and Secure Distributed Transaction Ledger Based on Proof-of-MarketabstractBlockchain technology has gained widespread use. However, it faces several challenges including throughput, transaction delay, security, and decentralization. This paper presents the Avalon protocol based on a novel Proof-of-Market (PoM) consensus mechanism to address these issues. PoM is a type of Proof-of-Work (PoW) consensus that incorporates market-driven leader election and shifts PoW from mining pools to consumers based on transactions. The matching incentive mechanism makes PoM incentive compatible. PoM decouples the scalability and security of Bitcoin, which means that Avalon can optimize the capacity and interval of blocks without compromising other performance goals. Our analysis shows that Avalon can tolerate malicious nodes possessing up to$\bf{1/3}$of the network's total computational power. Furthermore, the implementation of Avalon is similar to Bitcoin and is highly concise. We evaluate the performance of Avalon through a simulated network of over$\bf{1,000}$nodes. Experimental results demonstrate that Avalon can achieve a throughput of$\bf{4,000}$TPS (transactions per second), which is significantly better than state-of-the-art schemes ($\bf{10\boldsymbol{\times}}$Bitcoin-NG,$\bf{5\boldsymbol{\times}}$ByzCoin, and$\bf{4\boldsymbol{\times}}$Algorand). Additionally, it has a transaction confirmation delay of up to$\bf{40}$s, which is twice better than Bitcoin-NG and ByzCoin while experiencing only minimal blockchain splits and maintaining excellent decentralization. Weilin Chen 0002, Wei Yang 0011, Lide Xue, Bingren Chen, Youwen Zhu, Liusheng Huang |
IEEE Trans. Computers | 4 |
| 2020 | TransNet: Training Privacy-Preserving Neural Network over Transformed Layer
Qijian He, Wei Yang 0011, Bingren Chen, Yangyang Geng, Liusheng Huang |
Proc. VLDB Endow. | 3 |