Privacy-Preserving Average Consensus With Beaver Triple and Communication Obfuscation

• 成果类型: Article
• 署名作者: Wang, Peng; Lu, Yang; Lian, Jianming; Pan, Lulu; Shao, Haibin; Li, Ning
• 署名单位: Shanghai Jiao Tong University; Lancaster University; United States Department of Energy (DOE); Oak Ridge National Laboratory
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3539731
• 发表日期: 2025
• 页码: 4801-4808
• 关键词: privacy; noise; Consensus algorithm; Multi-agent systems; ELECTRONIC MAIL; accuracy; training; TOPOLOGY; Power supplies; Perturbation methods; Beaver triple; consensus; multiagent system; privacy

摘要

A privacy-preserving average consensus algorithm is proposed that synergizes the Beaver triple in secret sharing theory and noise obfuscation. The algorithm safeguards the initial values of agents against passive adversaries in a multiagent system. It is proved that the proposed algorithm can concurrently ensure average consensus and privacy, while also reducing the online computation and communication overhead compared to encryption-based ones. In addition, it imposes a less stringent condition for privacy preservation compared to certain noise-obfuscation techniques.