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Jinling Tang

dblp:362/3553 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2026
0009-0006-8507-2944ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 2 · 1 first-author · 2 since 2021Systems, architecture and hardware · 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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%
Network and information security
1 paper
Cryptographic protocols and secure computation · 100%
Artificial intelligence
1 paper
Efficient and distributed learning · 100%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed systems › fault tolerance
byzantine fault tolerance
1.012026
NunChuck: A Two-Phase Chained BFT Protocol for Tolerating the Partitioning Attack · IEEE Trans. Dependable Secur. Comput. 2026
Distributed systems
consensus
1.012026
NunChuck: A Two-Phase Chained BFT Protocol for Tolerating the Partitioning Attack · IEEE Trans. Dependable Secur. Comput. 2026
Distributed systems › fault tolerance › byzantine fault tolerance
view change
1.012026
NunChuck: A Two-Phase Chained BFT Protocol for Tolerating the Partitioning Attack · IEEE Trans. Dependable Secur. Comput. 2026
Cryptographic protocols and secure computation
secure aggregation
0.812024
A Flexible and Scalable Malicious Secure Aggregation Protocol for Federated Learning · IEEE Trans. Inf. Forensics Secur. 2024
Distributed systems
fault tolerance
0.312026
NunChuck: A Two-Phase Chained BFT Protocol for Tolerating the Partitioning Attack · IEEE Trans. Dependable Secur. Comput. 2026
Machine learning › Efficient and distributed learning
federated learning
0.212024
A Flexible and Scalable Malicious Secure Aggregation Protocol for Federated Learning · IEEE Trans. Inf. Forensics Secur. 2024
Cryptographic protocols and secure computation
commitment schemes
0.212024
A Flexible and Scalable Malicious Secure Aggregation Protocol for Federated Learning · IEEE Trans. Inf. Forensics Secur. 2024

Methods — techniques the papers use, named apart from their topics

shamir secret sharing · 1.5commitment scheme · 1.5formal safety and liveness proofs · 1.0certificate-based view-change · 1.0
YearPublicationVenuePosition
2026 PChain: A scalable and efficient parallel-chain consensus protocol
Huimei Liao, Haixia Xu 0002, Siyuan Leng, Jinling Tang, Yaobing Xu, Yinchang Zhou
Future Gener. Comput. Syst.4
2026 NunChuck: A Two-Phase Chained BFT Protocol for Tolerating the Partitioning Attack
abstract
Popular chained Byzantine Fault Tolerant (BFT) consensus protocols adopt a pipelined paradigm to achieve efficiency. However, these protocols remain vulnerable to critical attacks, such as forking and partitioning. While the two-phase commit rule can prevent forking, it often requires complex or expensive view-change mechanisms. Moreover, our study confirms that partitioning attacks are feasible in many existing chained BFT protocols. This paper devised NunChuck, a fully two-phase chained BFT protocol that ensures linear authenticator complexity, optimistic responsiveness, and resilience to partitioning attacks. NunChuck employs a lightweight certificate-based view-change mechanism to maintain efficiency, and introduces a unified message structure that enables leaders to make progress even under partitioned network conditions. We formally prove its safety and liveness properties. Furthermore, we develop a prototype implementation in Golang and conduct extensive evaluations. The results demonstrate that NunChuck consistently outperforms HotStuff and FastHotStuff, particularly in adversarial environments, with simulated delay analysis further corroborating our experimental findings.
Huimei Liao, Haixia Xu 0002, Mingsheng Wang, Jinling Tang, Siyuan Leng, Chunying Peng
IEEE Trans. Dependable Secur. Comput.4
2024 A Flexible and Scalable Malicious Secure Aggregation Protocol for Federated Learning
abstract
Secure aggregation becomes a major solution to providing privacy for federated learning. Secure aggregation for mobile devices typically relies on Shamir secret sharing (SSS) to achieve dropout robustness, but limits the system’s corruption and dropout tolerance. Although Prio+, a state-of-the-art method utilizing two non-colluding servers, avoids such limitations, its effectiveness is only against honest-but-curious servers. Thus, this paper presents a novel secure aggregation protocol in the malicious model. The proposed protocol uses a non-colluding server and initiator to achieve almost full (up ton-2) corruption and dropout tolerance, and exploits our discrete-logarithm (DL) extractable and equivocable commitment scheme to achieve malicious security. The proposed protocol’s security is proven in two models: malicious users colluding with the server and malicious users colluding with the initiator. Finally, a prototype of the developed protocol is implemented, with the experimental results demonstrating that our protocol is efficient and suitable for both cross-device and cross-silo federated learning scenarios. Compared with the sum protocol of Prio+, the proposed protocol achieves malicious security with affordable additional overhead, i.e., 4.8 to 6.1 times more computation cost and 2.8 to 2.9 times more communication cost for a single user.
Jinling Tang, Haixia Xu 0002, Mingsheng Wang, Chunying Peng, Huimei Liao
IEEE Trans. Inf. Forensics Secur.1