Preserving Topology Privacy of Network Systems by Feedback: Conditions and Distributed Design
For network systems using consensus protocols, this work addresses the challenge of protecting sensitive topology information from inference attacks while maintaining system functionality.
This paper proposes a feedback-based method to preserve topology privacy in network consensus protocols by violating topology identifiability conditions, achieving a controllable tradeoff between consensus deviation and privacy. Comparative simulations validate the effectiveness and outperformance of the proposed design.
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.