A Distributed Topology-Protecting Collaboration Algorithm: Design and Performance Analysis
针对多智能体系统易受拓扑推断攻击的隐私问题，提出一种分布式协作算法，通过构造新型噪声项和子拓扑选择策略，在保证收敛的同时降低拓扑推断精度。
The interaction topology of multiagent systems (MASs) is crucial for effective collaboration. Recent advances in topology inference provide a better understanding of the behaviors of the systems. Nevertheless, external attackers can exploit such techniques, posing a severe privacy breach, while the challenges to the topology protection problem remain unresolved. This article proposes a distributed collaboration algorithm for MASs to defend against topology inference attacks. The novelties include: 1) Compared with traditional noise-adding methods that inject decaying random inputs, the proposed algorithm constructs a novel noise term to increase the irregularity of the agents' states in a distributed manner while satisfying the convergence requirements. 2) A weight-selecting strategy is designed to choose the subtopologies to degrade the topology inference accuracy, further improving the topology-protecting performance. Theoretically, we derive the mean-square convergence factor and the nonasymptotic error bounds of our proposed algorithm. Extensive simulations demonstrate the effectiveness of the proposed algorithm in protecting the topology.