Privacy-Preserving Average Consensus in Multiagent Systems for Node Collusion

Wang Yaqi, Ye Jiabei, Lu Yue

TL;DR: We propose a novel algorithm to achieve the privacy-preserving average consensus against node collusion.

Abstract:

Average consensus problem of multiagent systems has always been an active topic, allowing multiple agents to interact with the average information of the initial values obtained locally. However, explicit sharing state variables may lead to privacy disclosure. In this brief, we deals with the problem of privacy preserving average consensus and node collusion for multiagent systems. Each agent passes subinformation to other agents through state decomposition and guarantees the average consensus of initial values. We show that privacy can be guaranteed under our approach, even if there is more than one curious node and they collude with each other. Finally, an example is provided to illustrate the design process and practical applications of the proposed approach.

Submission Number: 116