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Differentially private distributed algorithms for stochastic aggregative games

Published:2022-08 Issue: Volume:142 Page:110440
ISSN:0005-1098
Container-title:Automatica
language:en
Short-container-title:Automatica

Author:

Wang Jimin,Zhang Ji-Feng,He Xingkang

Funder

National Natural Science Foundation of China

Ministry of Science and Technology of the People's Republic of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Reference43 articles.

1. Abadi, M., Chu, A., Goodfellow, I., McMahan, H., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security (pp. 308–318).

2. Privacy-preserving multi-period demand response: A game theoretic approach;Alshehri,2019

3. A system-theoretic framework for privacy preservation in continuous-time multiagent dynamics;Altafini;Automatica,2020

4. Bassily, R., Feldman, V., & Talwar, K. (2019). Private stochastic convex optimization with optimal rates. In Advances in neural information processing systems. Vancouver, Canada.

5. Dong, R., Krichene, W., Bayen, A. M., & Sastry, S. S. (2015). Differential privacy of populations in routing games. In IEEE 54th annual conference on decision and control (pp. 2798–2803).

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3. Ensuring Both Almost Sure Convergence and Differential Privacy in Nash Equilibrium Seeking on Directed Graphs;IEEE Transactions on Automatic Control;2024-08

4. Differentially Private Distributed Algorithms for Aggregative Games With Guaranteed Convergence;IEEE Transactions on Automatic Control;2024-08

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