Differentially Private Distributed Mismatch Tracking Algorithm for Constraint-Coupled Resource Allocation Problems
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918044747431936 |
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| author | Wu, Wenwen Zhu, Shanying Liu, Shuai Guan, Xinping |
| author_facet | Wu, Wenwen Zhu, Shanying Liu, Shuai Guan, Xinping |
| contents | 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 ε-differentially private.And we also show the trade-off between convergence accuracy and privacy level. Finally, a numerical example is provided for verification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_07330 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Differentially Private Distributed Mismatch Tracking Algorithm for Constraint-Coupled Resource Allocation Problems Wu, Wenwen Zhu, Shanying Liu, Shuai Guan, Xinping Optimization and Control Systems and Control Signal Processing 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 ε-differentially private.And we also show the trade-off between convergence accuracy and privacy level. Finally, a numerical example is provided for verification. |
| title | Differentially Private Distributed Mismatch Tracking Algorithm for Constraint-Coupled Resource Allocation Problems |
| topic | Optimization and Control Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2204.07330 |