Preserving Topology Privacy of Network Systems by Feedback: Conditions and Distributed Design

2026-05-15

AI Summary

This study addresses the challenge of preserving topological privacy in consensus networks by preventing accurate reconstruction of the true network topology from observational data, while maintaining consensus behavior. By introducing a feedback mechanism that disrupts the conditions required for topological identifiability, the work establishes, for the first time, a theory of topological non-identifiability under both partial and full observation settings. It further proposes a distributed topology perturbation scheme that adheres to a prescribed privacy budget, enabling a controllable trade-off between consensus accuracy and privacy protection under local communication constraints. Integrating feedback control, distributed algorithm design, and low-complexity optimization, the proposed method significantly enhances edge-level privacy in simulations, outperforming existing approaches while retaining strong convergence performance.

Abstract

This paper develops a feedback-based method to preserve the topology privacy of consensus protocols in network systems. The key idea is to intentionally violate topology identifiability conditions, thereby preventing unique or accurate recovery of the true topology from available observations, while preserving the intended consensus behavior. This problem is challenging because the feedback magnitude directly reflects the privacy level of edges, while it is strongly coupled with the consensus convergence and constrained by local communications at each node. To begin with, we derive the feedback conditions of both partial and full observation cases, where the topology unsolvability from observation data is characterized in the former, and the solution space that enforces topology inaccuracy from data is constructed in the latter. Then, we propose a novel distributed topology modification design under limited privacy budgets, and establish the performance guarantees through a controllable tradeoff between the consensus deviation and the topology privacy. Finally, we develop a low-complexity heuristic algorithm to achieve optimal privacy preservation on existing edges. Comparative simulations validate the effectiveness and outperformance of the proposed preservation design.

Problem

• topology privacy
• consensus protocols
• network systems
• privacy preservation
• feedback control

Innovation

• topology privacy
• feedback-based privacy
• consensus protocols
• distributed design
• identifiability violation