Differentially Private Average Consensus for Networks With Positive Agents

This research paper addresses the problem of achieving differentially private average consensus for multiagent systems (MASs) consisting of positive agents. A novel randomized mechanism is introduced that employs nondecaying positive multiplicative truncated Gaussian noises to maintain the positivity and randomness of the state information over time. A time-varying controller is developed to achieve mean-square positive average consensus, and convergence accuracy is evaluated. The proposed mechanism is shown to preserve
$(\epsilon,\delta)$
-differential privacy of MASs, and the privacy budget is derived. Numerical examples are provided to illustrate the effectiveness of the proposed controller and privacy mechanism.