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.
Authors
- Shanying Zhu (ORCID: https://orcid.org/0000-0003-4860-4519)
- Wen‐An Zhang (ORCID: https://orcid.org/0000-0002-4355-2783)
- Ning Zheng (ORCID: https://orcid.org/0000-0003-3503-8167)
- Yiming Wu (ORCID: https://orcid.org/0000-0001-9766-2307)
- C. F. Ying (ORCID: https://orcid.org/0000-0001-8980-2314)
Institutions
- Shanghai Jiao Tong University (CN)
- State Key Laboratory of Industrial Control Technology (CN)
- Hangzhou Dianzi University (CN)
- Zhejiang University of Technology (CN)
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
- National Natural Science Foundation of China