Differentially Private Average Consensus for Networks With Positive Agents
针对由正智能体组成的多智能体系统，提出一种使用非衰减正乘性截断高斯噪声的随机机制，实现差分隐私平均一致性，并评估了收敛精度和隐私预算。
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 (ϵ,δ) -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.