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Secure Cloud Computing Algorithms for Discrete Constrained Potential Games

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Secure Cloud Computing Algorithms for Discrete Constrained Potential Games. / Lu, Yang; Zhu, Minghui.
IFAC Workshop on Distributed Estimation and Control in Networked Systems. Vol. 48 22. ed. Elsevier, 2015. p. 180-185.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Lu, Y & Zhu, M 2015, Secure Cloud Computing Algorithms for Discrete Constrained Potential Games. in IFAC Workshop on Distributed Estimation and Control in Networked Systems. 22 edn, vol. 48, Elsevier, pp. 180-185, 5th IFAC Workshop on Distributed Estimation and Control in Networked Systems NecSys 2015, Philadelphia, Pennsylvania, United States, 10/09/15. https://doi.org/10.1016/j.ifacol.2015.10.327

APA

Lu, Y., & Zhu, M. (2015). Secure Cloud Computing Algorithms for Discrete Constrained Potential Games. In IFAC Workshop on Distributed Estimation and Control in Networked Systems (22 ed., Vol. 48, pp. 180-185). Elsevier. https://doi.org/10.1016/j.ifacol.2015.10.327

Vancouver

Lu Y, Zhu M. Secure Cloud Computing Algorithms for Discrete Constrained Potential Games. In IFAC Workshop on Distributed Estimation and Control in Networked Systems. 22 ed. Vol. 48. Elsevier. 2015. p. 180-185 doi: 10.1016/j.ifacol.2015.10.327

Author

Lu, Yang ; Zhu, Minghui. / Secure Cloud Computing Algorithms for Discrete Constrained Potential Games. IFAC Workshop on Distributed Estimation and Control in Networked Systems. Vol. 48 22. ed. Elsevier, 2015. pp. 180-185

Bibtex

@inproceedings{6f2c9248e0894d089d0ce4e157067c07,
title = "Secure Cloud Computing Algorithms for Discrete Constrained Potential Games",
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.",
keywords = "Secure computation, cloud computing, potential games, reinforcement learning, homomorphic encryption",
author = "Yang Lu and Minghui Zhu",
year = "2015",
month = sep,
day = "11",
doi = "10.1016/j.ifacol.2015.10.327",
language = "English",
volume = "48",
pages = "180--185",
booktitle = "IFAC Workshop on Distributed Estimation and Control in Networked Systems",
publisher = "Elsevier",
address = "Netherlands",
edition = "22",
note = "5th IFAC Workshop on Distributed Estimation and Control in Networked Systems NecSys 2015 ; Conference date: 10-09-2015 Through 11-09-2015",
url = "https://www.sciencedirect.com/journal/ifac-papersonline/vol/48/issue/22",

}

RIS

TY - GEN

T1 - Secure Cloud Computing Algorithms for Discrete Constrained Potential Games

AU - Lu, Yang

AU - Zhu, Minghui

PY - 2015/9/11

Y1 - 2015/9/11

N2 - 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.

AB - 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.

KW - Secure computation

KW - cloud computing

KW - potential games

KW - reinforcement learning

KW - homomorphic encryption

U2 - 10.1016/j.ifacol.2015.10.327

DO - 10.1016/j.ifacol.2015.10.327

M3 - Conference contribution/Paper

VL - 48

SP - 180

EP - 185

BT - IFAC Workshop on Distributed Estimation and Control in Networked Systems

PB - Elsevier

T2 - 5th IFAC Workshop on Distributed Estimation and Control in Networked Systems NecSys 2015

Y2 - 10 September 2015 through 11 September 2015

ER -