Dynamics-Based Algorithm-Level Privacy Preservation for Push-Sum Average Consensus
Huqiang Cheng, Mengying Xie, Xiaowei Yang, Qingguo L\"u, and Huaqing, Li

TL;DR
This paper introduces a novel privacy-preserving average consensus algorithm for multi-agent systems on unbalanced directed networks, embedding randomness and auxiliary parameters to ensure privacy without sacrificing convergence speed.
Contribution
The paper presents a new algorithm that guarantees exact average consensus while preserving privacy against various attacks, differing from differential privacy approaches.
Findings
Converges linearly to the exact average consensus value.
Guarantees privacy against honest-but-curious and eavesdropping attacks.
Validated by numerical experiments.
Abstract
In the intricate dance of multi-agent systems, achieving average consensus is not just vital--it is the backbone of their functionality. In conventional average consensus algorithms, all agents reach an agreement by individual calculations and sharing information with their respective neighbors. Nevertheless, the information interactions that occur in the communication network may make sensitive information be revealed. In this paper, we develop a new privacy-preserving average consensus method on unbalanced directed networks. Specifically, we ensure privacy preservation by carefully embedding randomness in mixing weights to confuse communications and introducing an extra auxiliary parameter to mask the state-updated rule in several initial iterations. In parallel, we exploit the intrinsic robustness of consensus dynamics to guarantee that the average consensus is precisely achieved.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDistributed Control Multi-Agent Systems · Opinion Dynamics and Social Influence · Opportunistic and Delay-Tolerant Networks
