Whole-Process Privacy-Preserving and Sybil-Resilient Consensus for Multiagent Networks

This article is concerned with the co-design of privacy-preserving and resilient consensus protocol for a class of multiagent networks (MANs), where the information exchanges over communication networks among the agents suffer from eavesdropping and Sybil attacks. First, we introduce a new attack model in which an adversarial agent could launch a Sybil attack, generating a large number of spurious entities in the network, thereby gaining disproportionate influence. In this communication framework, a whole-process privacy-preserving mechanism is designed that is capable of protecting both initial and current states of agents. Then, instead of existing methods requiring identifying and mitigating Sybil nodes, a degree-based mean-subsequence-reduced (D-MSR) resilient strategy is implemented, showcasing its significant properties: 1) ensuring the effectiveness of aforementioned designed privacy protection strategy; 2) allowing the network to contain Sybil nodes without elimination; and 3) reaching consensus among the normal agents. Finally, several numerical simulations are provided to validate the effectiveness of the proposed results.

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Publication Details

Journal
IEEE Transactions on Neural Networks and Learning Systems
Published
2024-11-05
DOI
https://doi.org/10.1109/tnnls.2024.3488115
Citations
3
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
2.34

Funders

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article

Whole-Process Privacy-Preserving and Sybil-Resilient Consensus for Multiagent Networks

Shanying Zhu, Wen‐An Zhang, Ning Zheng, Yiming Wu et al.
3 citations
IEEE Transactions on Neural Networks and Learning Systems
Blockchain Technology Applications and Security
2.34
article

Whole-Process Privacy-Preserving and Sybil-Resilient Consensus for Multiagent Networks

Shanying Zhu, Wen‐An Zhang, Ning Zheng, Yiming Wu, C. F. Ying
article en
3 citations

Abstract

This article is concerned with the co-design of privacy-preserving and resilient consensus protocol for a class of multiagent networks (MANs), where the information exchanges over communication networks among the agents suffer from eavesdropping and Sybil attacks. First, we introduce a new attack model in which an adversarial agent could launch a Sybil attack, generating a large number of spurious entities in the network, thereby gaining disproportionate influence. In this communication framework, a whole-process privacy-preserving mechanism is designed that is capable of protecting both initial and current states of agents. Then, instead of existing methods requiring identifying and mitigating Sybil nodes, a degree-based mean-subsequence-reduced (D-MSR) resilient strategy is implemented, showcasing its significant properties: 1) ensuring the effectiveness of aforementioned designed privacy protection strategy; 2) allowing the network to contain Sybil nodes without elimination; and 3) reaching consensus among the normal agents. Finally, several numerical simulations are provided to validate the effectiveness of the proposed results.

IEEE Transactions on Neural Networks and Learning SystemsVol. 36(7)
Shanghai Jiao Tong University (CN), State Key Laboratory of Industrial Control Technology (CN), Hangzhou Dianzi University (CN), Zhejiang University of Technology (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 9%
Blockchain Technology Applications and Security
2.34
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Whole-Process Privacy-Preserving and Sybil-Resilient Consensus for Multiagent Networks — Shanying Zhu, Wen‐An Zhang, et al. · IEEE Transactions on Neural Networks and Learning Systems (2024) | TGRS Research Map | TGRS