Privacy-Preserving Resilient Vector Consensus
成果类型：
Article
署名作者：
Liu, Bing; Zhao, Chengcheng; Chai, Li; Cheng, Peng; Chen, Jiming
署名单位：
Zhejiang University; Hangzhou Dianzi University
刊物名称：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
ISSN/ISSBN：
0018-9286
DOI：
10.1109/TAC.2025.3580300
发表日期：
2025
关键词：
摘要：
In this article, we study privacy-preserving resilient vector consensus in multiagent systems against faulty agents, where normal agents can achieve consensus within the convex hull of their initial states while protecting state vectors from being disclosed. Specifically, we consider a modification of an existing algorithm known as approximate distributed robust convergence using centerpoints (ADRC), i.e., privacy-preserving ADRC (PP-ADRC). Under PP-ADRC, each normal agent introduces multivariate Gaussian noise to its state during each iteration. We first provide sufficient conditions to ensure that all normal agents' states can achieve mean square convergence under PP-ADRC. Then, we analyze convergence accuracy from two perspectives, i.e., the Mahalanobis distance of the final value from its expectation and the Hausdorff distance-based alteration of the convex hull caused by noise when only partial dimensions are added with noise. Then, we employ concentrated geo-privacy to characterize privacy preservation and conduct a thorough comparison with differential privacy. Finally, numerical simulations demonstrate the theoretical results.