Private bipartite consensus control for multi-agent systems: A hierarchical differential privacy scheme

This paper addresses the differentially private bipartite consensus control problem for general linear discretetime multi-agent systems (MASs), where the initial states of the agents are the sensitive data to be protected from potential eavesdroppers. In response to the cooperative-competitive relationship among the agents, a novel hierarchical differential privacy mechanism (HDPM) is proposed, which consists of a classifier and a pair of input and output noise injectors. The HDPM and the bipartite consensus controller are co-designed to ensure the ultimate mean-square bipartite consensus while preserving differential privacy. Finally, numerical simulations are conducted to illustrate the effectiveness of the proposed algorithm.