Algorithm-Level Confidentiality for Average Consensus on Time-Varying Directed Graphs
Algorithm-Level Confidentiality for Average Consensus on Time-Varying Directed Graphs
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DOI：
10.1109/tnse.2022.3140274
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发表时间：
2022-01
影响因子：
6.6
通讯作者：
Huan Gao;Yongqiang Wang
中科院分区：
文献类型：
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作者：
Huan Gao;Yongqiang Wang
平均共识在分布式网络中起着关键作用，其应用范围从时间同步、信息融合、负载平衡到分散控制。现有的平均共识算法要求个体代理与其邻居交换显式的状态值，这导致了状态中敏感信息的泄露。在本文中，我们提出了一种新的时变有向图的平均共识算法，该算法可以保护参与代理对其他参与代理的机密性。该算法在交互过程中注入随机性，在算法层面混淆信息，在不需要任何可信第三方或数据聚合器的帮助下保证信息的理论性隐私。通过利用共识动态对交互随机变化的固有鲁棒性，我们提出的算法还可以保证平均共识的准确性。该算法明显不同于基于差分隐私的平均共识方法，后者通过牺牲获得的共识值的准确性来实现机密性。数值模拟验证了该方法的有效性和高效性。
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.