Differential Privacy Optimal Consensus for Multiagent System by Using Functional Perturbation

Optimal consensus algorithm is affective algorithm using for distributed system, which makes each agent realize consensus and minimize the cost function. However, cooperative control requires that the agents exchange information in the public channel, which means that the important information disclosure may appear. To solve the problem, in this paper, we propose a differential privacy optimal algorithm by adding functional perturbation. By using the convex properties, the mean square consensus conditions are obtained. In addition, we analyze the privacy-preserving level and compute the sensitivity of the differential privacy at the same time. At last, the simulation is given to show the effectiveness of the theoretical results.