Average consensus is essential for multi-agent systems to achieve specific functions and is widely used in network control, information fusion, etc. In conventional average consensus algorithms, all agents reach an agreement by individual calculations and sharing information with their respective neighbors. Nevertheless, the information interactions that occur in the communication network may make privacy information be revealed. In this paper, we develop a new privacy-preserving average consensus method for unbalanced digraphs. Specifically, we ensure privacy preservation by carefully embedding randomness in mixing weights to confuse communications and introducing an extra auxiliary parameter to mask the state-updated rule in initial several iterations. In parallel, we exploit the intrinsic robustness of consensus dynamics to guarantee that the average consensus is precisely achieved. Theoretical results demonstrate that the designed algorithms can converge linearly to the exact average consensus value and can guarantee privacy preservation of agents against both honest-but-curious and eavesdropping attacks. The designed algorithms are fundamentally different compared to differential privacy based algorithms that enable privacy preservation via sacrificing consensus performance. Finally, numerical experiments validate the correctness of the theoretical findings.

翻译：平均一致性是多智能体系统实现特定功能的基础，广泛应用于网络控制、信息融合等领域。在传统平均一致性算法中，所有智能体通过独立计算并与邻居交换信息达成一致。然而，通信网络中的信息交互可能导致隐私信息泄露。本文针对非平衡有向图提出一种新型隐私保护平均一致性方法。具体而言，我们通过在混合权重中精心嵌入随机性以混淆通信，并在初始若干次迭代中引入额外辅助参数掩盖状态更新规则，从而确保隐私保护。同时，利用一致性动力学的固有鲁棒性保证平均一致性的精确实现。理论结果表明，所设计算法能够线性收敛至精确平均一致性值，并能在面对诚实但好奇攻击及窃听攻击时保障智能体隐私。该算法与通过牺牲一致性性能实现隐私保护的差分隐私算法存在本质区别。最后，数值实验验证了理论结果的正确性。