Abstract
This paper considers a private-preserving issue of distributed Nash equilibrium seeking algorithms over directed graph, assuming the objective function is sensitive. In order to ensure the privacy of individuals, agents’s information is interfered by independent random noises that obey Laplace distribution, and then sent to their neighbors. By resorting to Perron-Frobenius (PF) theorem, a fully distributed algorithm is established with perturbed local information, where the weights of the PF eigenvector are adopted. Since the mappings are strongly monotone and Lipschitz continuous, the algorithm we proposed guarantees the asymptotic convergence of NE and keeps the information privacy of each agent. Furthermore, the proposed algorithm is proved to be \(\epsilon \)-differentially private and the value of \(\epsilon \) can be obtained. Finally, the above conclusion is verified by numerical simulation.
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Zhang, H., Xu, W., Rao, X. (2023). Privacy-Preserving Nash Equilibrium Seeking Algorithm over Directed Graph. In: Ren, Z., Wang, M., Hua, Y. (eds) Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control. Lecture Notes in Electrical Engineering, vol 934. Springer, Singapore. https://doi.org/10.1007/978-981-19-3998-3_81
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DOI: https://doi.org/10.1007/978-981-19-3998-3_81
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