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

TL;DR

This paper introduces a new metric to evaluate how well network topology can be preserved against inference attacks using noise, and proposes optimal noise strategies that improve non-asymptotic preservation performance.

Contribution

It proposes a novel trace-based variance-expectation ratio metric and designs optimal noise schemes, including one-lag dependence, to enhance topology privacy in network systems.

Findings

The new metric effectively measures the decay rate of topology inference error.

Optimal noise design with one-lag dependence achieves zero state deviation.

Simulations confirm the theoretical advantages of the proposed methods.

Abstract

Preserving the topology from being inferred by external adversaries has become a paramount security issue for network systems (NSs), and adding random noises to the nodal states provides a promising way. Nevertheless, recent works have revealed that the topology cannot be preserved under i.i.d. noises in the asymptotic sense. How to effectively characterize the non-asymptotic preservation performance still remains an open issue. Inspired by the deviation quantification of concentration inequalities, this paper 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 non-asymptotic preservation performance. We prove that the inference error will always decay to zero asymptotically, as long as the added noises are non-increasing 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 non-zero topology inference error in the asymptotic sense simultaneously. Extensions to a general class of noises with multi-lag time dependence are provided. Comprehensive simulations verify the theoretical findings.