Differentially Private Dynamic Average Consensus of Multiagent Systems via Cloud Access With Self-Triggered Mechanism

Sensitive information is critical in networked systems, and a differential privacy problem of dynamic average consensus (DAC) is reported for a class of multiagent systems that cannot communicate directly. With the help of a cloud repository, a cloud-based differentially private DAC control scheme is proposed. Agents' states are added noises, and combined states are constructed. With combined state errors, a self-triggered rule for a cloud access schedule is proposed in a cloud framework. With this proposed scheme, the related system consensus accuracy and privacy-preserving index are analyzed. Furthermore, the relationship, between the privacy-preserving index and parameters in a cloud access scheduling rule, is investigated. Finally, two examples are given to demonstrate the feasibility of the scheme.