Design of Privacy Mechanism for Cyber Physical Systems: A Nash Q-learning Approach

Qirui Zhang; Siqi Meng; Kun Liu; Wei Dai

doi:10.1109/CAC57257.2022.10054655

Design of Privacy Mechanism for Cyber Physical Systems: A Nash Q-learning Approach

Qirui Zhang, Siqi Meng, Kun Liu^*, Wei Dai

^*此作品的通讯作者

自动化学院

China University of Mining and Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

This paper studies the problem of designing optimal privacy mechanism with less energy cost. The eavesdropper and the defender with limited resources should choose which channel to eavesdrop and defend, respectively. A zero-sum stochastic game framework is used to model the interaction between the two players and the game is solved through the Nash Q-learning approach. A numerical example is given to verify the proposed method.

源语言	英语
主期刊名	Proceedings - 2022 Chinese Automation Congress, CAC 2022
出版商	Institute of Electrical and Electronics Engineers Inc.
页	6361-6365
页数	5
ISBN（电子版）	9781665465335
DOI	https://doi.org/10.1109/CAC57257.2022.10054655
出版状态	已出版 - 2022
活动	2022 Chinese Automation Congress, CAC 2022 - Xiamen, 中国期限: 25 11月 2022 → 27 11月 2022

出版系列

姓名	Proceedings - 2022 Chinese Automation Congress, CAC 2022
卷	2022-January

会议

会议	2022 Chinese Automation Congress, CAC 2022
国家/地区	中国
市	Xiamen
时期	25/11/22 → 27/11/22

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

访问文件

10.1109/CAC57257.2022.10054655

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhang, Q., Meng, S., Liu, K., & Dai, W. (2022). Design of Privacy Mechanism for Cyber Physical Systems: A Nash Q-learning Approach. 在 Proceedings - 2022 Chinese Automation Congress, CAC 2022 (页码 6361-6365). (Proceedings - 2022 Chinese Automation Congress, CAC 2022; 卷 2022-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAC57257.2022.10054655

@inproceedings{c39cbc24cc014827a65925f1b15be06d,

title = "Design of Privacy Mechanism for Cyber Physical Systems: A Nash Q-learning Approach",

abstract = "This paper studies the problem of designing optimal privacy mechanism with less energy cost. The eavesdropper and the defender with limited resources should choose which channel to eavesdrop and defend, respectively. A zero-sum stochastic game framework is used to model the interaction between the two players and the game is solved through the Nash Q-learning approach. A numerical example is given to verify the proposed method.",

keywords = "Nash Q-learning, Privacy mechanism, stochastic game",

author = "Qirui Zhang and Siqi Meng and Kun Liu and Wei Dai",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 Chinese Automation Congress, CAC 2022 ; Conference date: 25-11-2022 Through 27-11-2022",

year = "2022",

doi = "10.1109/CAC57257.2022.10054655",

language = "English",

series = "Proceedings - 2022 Chinese Automation Congress, CAC 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "6361--6365",

booktitle = "Proceedings - 2022 Chinese Automation Congress, CAC 2022",

address = "United States",

}

Zhang, Q, Meng, S, Liu, K & Dai, W 2022, Design of Privacy Mechanism for Cyber Physical Systems: A Nash Q-learning Approach. 在 Proceedings - 2022 Chinese Automation Congress, CAC 2022. Proceedings - 2022 Chinese Automation Congress, CAC 2022, 卷 2022-January, Institute of Electrical and Electronics Engineers Inc., 页码 6361-6365, 2022 Chinese Automation Congress, CAC 2022, Xiamen, 中国, 25/11/22. https://doi.org/10.1109/CAC57257.2022.10054655

Design of Privacy Mechanism for Cyber Physical Systems: A Nash Q-learning Approach. / Zhang, Qirui; Meng, Siqi; Liu, Kun 等.
Proceedings - 2022 Chinese Automation Congress, CAC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. 页码 6361-6365 (Proceedings - 2022 Chinese Automation Congress, CAC 2022; 卷 2022-January).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Design of Privacy Mechanism for Cyber Physical Systems

T2 - 2022 Chinese Automation Congress, CAC 2022

AU - Zhang, Qirui

AU - Meng, Siqi

AU - Liu, Kun

AU - Dai, Wei

PY - 2022

Y1 - 2022

N2 - This paper studies the problem of designing optimal privacy mechanism with less energy cost. The eavesdropper and the defender with limited resources should choose which channel to eavesdrop and defend, respectively. A zero-sum stochastic game framework is used to model the interaction between the two players and the game is solved through the Nash Q-learning approach. A numerical example is given to verify the proposed method.

AB - This paper studies the problem of designing optimal privacy mechanism with less energy cost. The eavesdropper and the defender with limited resources should choose which channel to eavesdrop and defend, respectively. A zero-sum stochastic game framework is used to model the interaction between the two players and the game is solved through the Nash Q-learning approach. A numerical example is given to verify the proposed method.

KW - Nash Q-learning

KW - Privacy mechanism

KW - stochastic game

UR - http://www.scopus.com/inward/record.url?scp=85151128838&partnerID=8YFLogxK

U2 - 10.1109/CAC57257.2022.10054655

DO - 10.1109/CAC57257.2022.10054655

M3 - Conference contribution

AN - SCOPUS:85151128838

T3 - Proceedings - 2022 Chinese Automation Congress, CAC 2022

SP - 6361

EP - 6365

BT - Proceedings - 2022 Chinese Automation Congress, CAC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 25 November 2022 through 27 November 2022

ER -

Design of Privacy Mechanism for Cyber Physical Systems: A Nash Q-learning Approach

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此