Preserving Topology of Network Systems: Metric, Analysis, and Optimal Design

• 成果类型: Article
• 署名作者: Li, Yushan; Wang, Zitong; He, Jianping; Chen, Cailian; Guan, Xinping
• 署名单位: Shanghai Jiao Tong University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3503501
• 发表日期: 2025
• 页码: 3540-3555
• 关键词: topology; noise; measurement; Network topology; Complexity theory; security; Network systems; Eigenvalues and eigenfunctions; Artificial neural networks; vectors; Decaying rate analysis; network systems (NSs); noise adding mechanism; topology inference; topology preservation

摘要

Preserving the topology from being inferred by external adversaries has become a paramount security issue for network systems, and adding random noises to the nodal states provides a promising way. Nevertheless, recent works have revealed that the topology cannot be preserved under independent identically distributed (i.i.d.) noises in the asymptotic sense. How to effectively characterize the nonasymptotic preservation performance still remains an open issue. Inspired by the deviation quantification of concentration inequalities, this article proposes a novel metric named trace-based variance-expectation ratio. This metric effectively captures the decaying rate of the topology inference error, where a slower rate indicates better nonasymptotic preservation performance. We prove that the inference error will always decay to zero asymptotically, as long as the added noises are nonincreasing and independent (milder than the i.i.d. condition). Then, the optimal noise design that produces the slowest decaying rate for the error is obtained. More importantly, we amend the noise design by introducing one-lag time dependence, achieving the zero state deviation and the nonzero topology inference error in the asymptotic sense simultaneously. Extensions to a general class of noises with multilag time dependence are provided. Comprehensive simulations verify the theoretical findings.