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Secure Cloud Computing Algorithms for Discrete Constrained Potential Games

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Published
Publication date11/09/2015
Host publicationIFAC Workshop on Distributed Estimation and Control in Networked Systems
PublisherElsevier
Pages180-185
Number of pages6
Volume48
Edition22
<mark>Original language</mark>English
Event5th IFAC Workshop on Distributed Estimation and Control in Networked Systems NecSys 2015 - Philadelphia, United States
Duration: 10/09/2015 → 11/09/2015
https://www.sciencedirect.com/journal/ifac-papersonline/vol/48/issue/22

Conference

Conference5th IFAC Workshop on Distributed Estimation and Control in Networked Systems NecSys 2015
Country/TerritoryUnited States
CityPhiladelphia
Period10/09/15 → 11/09/15
Internet address

Conference

Conference5th IFAC Workshop on Distributed Estimation and Control in Networked Systems NecSys 2015
Country/TerritoryUnited States
CityPhiladelphia
Period10/09/15 → 11/09/15
Internet address

Abstract

In this paper, we study secure cloud computing problem for a class of discrete constrained potential games. In the games, certain functions are confidential for the system operator and not disclosed to any other participant. Meanwhile, each agent is unwilling to disclose its private functions and states to any other participant. By utilizing reinforcement learning and homomorphic encryption, we propose a distributed algorithm where (i) both the confidentiality for the system operator and the privacy for the agents are protected; (ii) the convergence to Nash equilibria is formally ensured.