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arXiv · 2204.07330

Differentially Private Distributed Mismatch Tracking Algorithm for Constraint-Coupled Resource Allocation Problems

Wenwen Wu·Shanying Zhu·Shuai Liu·Xinping Guan
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Abstract

This paper considers privacy-concerned distributed constraint-coupled resource allocation problems over an undirected network, where each agent holds a private cost function and obtains the solution via only local communication. With privacy concerns, we mask the exchanged information with independent Laplace noise against a potential attacker with potential access to all network communications. We propose a differentially private distributed mismatch tracking algorithm (diff-DMAC) to achieve cost-optimal distribution of resources while preserving privacy. Adopting constant stepsizes, the linear convergence property of diff-DMAC in mean square is established under the standard assumptions of Lipschitz gradients and strong convexity. Moreover, it is theoretically proven that the proposed algorithm is {\epsilon}-differentially private.And we also show the trade-off between convergence accuracy and privacy level. Finally, a numerical example is provided for verification.

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Wenwen Wu, Shanying Zhu, Shuai Liu, Xinping Guan. 2022-04-15. Differentially Private Distributed Mismatch Tracking Algorithm for Constraint-Coupled Resource Allocation Problems. https://doi.org/10.1109/cdc51059.2022.9993173.

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