论文标题
算法级级的机密性，以平均有针对性的图表共识
Algorithm-Level Confidentiality for Average Consensus on Time-Varying Directed Graphs
论文作者
论文摘要
平均共识在分布式网络中起关键作用，应用程序从时间同步，信息融合，负载平衡到分散控制范围。现有的平均共识算法要求各个代理与邻居交换明确的状态值，这导致了该州敏感信息的不良披露。在本文中，我们提出了一种新颖的平均共识算法，以实现时变的定向图，可以保护参与代理的机密性，以防止其他参与代理。该算法在交互中注入随机性，以混淆算法级别的信息，并可以在没有任何受信任的第三方或数据聚合器的帮助的情况下确保信息理论隐私。通过利用共识动力学对相互作用的随机变化的固有鲁棒性，我们提出的算法也可以保证平均共识的准确性。该算法与基于差异性的平均共识方法明显不同，这些方法通过损害获得的共识值来促进机密性。数值模拟证实了我们提出的方法的有效性和效率。
Average consensus plays a key role in distributed networks, with applications ranging from time synchronization, information fusion, load balancing, to decentralized control. Existing average consensus algorithms require individual agents to exchange explicit state values with their neighbors, which leads to the undesirable disclosure of sensitive information in the state. In this paper, we propose a novel average consensus algorithm for time-varying directed graphs that can protect the confidentiality of a participating agent against other participating agents. The algorithm injects randomness in interaction to obfuscate information on the algorithm-level and can ensure information-theoretic privacy without the assistance of any trusted third party or data aggregator. By leveraging the inherent robustness of consensus dynamics against random variations in interaction, our proposed algorithm can also guarantee the accuracy of average consensus. The algorithm is distinctly different from differential-privacy based average consensus approaches which enable confidentiality through compromising accuracy in obtained consensus value. Numerical simulations confirm the effectiveness and efficiency of our proposed approach.
