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Sarah K. Spurgeon

Publications and source records attributed to Sarah K. Spurgeon.

2 recordsLinked to original sources

Can Inherent Communication Noise Guarantee Privacy in Distributed Cooperative Control ?

This paper investigates privacy-preserving distributed cooperative control for multi-agent systems within the framework of differential privacy. In cooperative control, communication noise is inevitable and is usually regarded as a disturbance that impairs coordination. This work revisits such noise as a potential privacy-enhancing factor. A linear quadratic regulator (LQR)-based framework is proposed for agents communicating over noisy channels, \textcolor{black}{where the noise variance depends on the relative state differences between neighbouring agents.} The resulting controller achieves formation while protecting the reference signals from inference attacks. It is analytically proven that the inherent communication noise can guarantee bounded $(ε,δ)$-differential privacy without adding dedicated privacy noise, while the \textcolor{black}{system cooperative tracking error} remains bounded and convergent in both the mean-square and almost-sure sense.

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Distributed Finite-Horizon Optimal Control for Consensus with Differential Privacy Guarantees

This paper addresses the problem of privacy-preserving consensus control for multi-agent systems (MAS) using differential privacy. We propose a novel distributed finite-horizon linear quadratic regulator (LQR) framework, in which agents share individual state information while preserving the confidentiality of their local pairwise weight matrices, which are considered sensitive data in MAS. Protecting these matrices effectively safeguards each agent's private cost function and control preferences. Our solution injects consensus error-dependent Laplace noise into the communicated state information and employs a carefully designed time-dependent scaling factor in the local cost functions. {This approach guarantees bounded consensus and achieves rigorous $ε$-differential privacy for the weight matrices without relying on specific noise distribution assumptions.} Additionally, we analytically characterize the trade-off between consensus accuracy and privacy level, offering clear guidelines on how to enhance consensus performance through appropriate scaling of the LQR weight matrices and the privacy budget.

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