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.
|Original language||English (US)|
|Number of pages||6|
|State||Published - Oct 1 2015|
|Event||5th IFAC Workshop on Distributed Estimation and Control in Networked Systems, NecSys 2015 - Philadelphia, United States|
Duration: Sep 10 2015 → Sep 11 2015
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering