Event-Triggered Differentially Private Average Consensus for Multi-agent Network

This paper investigates the differentially private problem of the average consensus for a class of discrete-time multi-agent network systems (MANSs). Based on the MANSs, a new distributed differentially private consensus algorithm (DPCA) is developed. To avoid continuous communication between neighboring agents, a kind of intermittent communication strategy depending on an event-triggered function is established in our DPCA. Based on our algorithm, we carry out the detailed analysis including its convergence, its accuracy, its privacy and the trade-off between the accuracy and the privacy level, respectively. It is found that our algorithm preserves the privacy of initial states of all agents in the whole process of consensus computation. The trade-off motivates us to find the best achievable accuracy of our algorithm under the free parameters and the fixed privacy level. Finally, numerical experiment results testify the validity of our theoretical analysis.