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

^*Corresponding author for this work

School of Automation

China University of Mining and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings - 2022 Chinese Automation Congress, CAC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6361-6365
Number of pages	5
ISBN (Electronic)	9781665465335
DOIs	https://doi.org/10.1109/CAC57257.2022.10054655
Publication status	Published - 2022
Event	2022 Chinese Automation Congress, CAC 2022 - Xiamen, China Duration: 25 Nov 2022 → 27 Nov 2022

Publication series

Name	Proceedings - 2022 Chinese Automation Congress, CAC 2022
Volume	2022-January

Conference

Conference	2022 Chinese Automation Congress, CAC 2022
Country/Territory	China
City	Xiamen
Period	25/11/22 → 27/11/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Nash Q-learning
Privacy mechanism
stochastic game

Access to Document

10.1109/CAC57257.2022.10054655

Cite this

Zhang, Q., Meng, S., Liu, K., & Dai, W. (2022). Design of Privacy Mechanism for Cyber Physical Systems: A Nash Q-learning Approach. In Proceedings - 2022 Chinese Automation Congress, CAC 2022 (pp. 6361-6365). (Proceedings - 2022 Chinese Automation Congress, CAC 2022; Vol. 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. in Proceedings - 2022 Chinese Automation Congress, CAC 2022. Proceedings - 2022 Chinese Automation Congress, CAC 2022, vol. 2022-January, Institute of Electrical and Electronics Engineers Inc., pp. 6361-6365, 2022 Chinese Automation Congress, CAC 2022, Xiamen, China, 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 et al.
Proceedings - 2022 Chinese Automation Congress, CAC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 6361-6365 (Proceedings - 2022 Chinese Automation Congress, CAC 2022; Vol. 2022-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 - https://www.scopus.com/pages/publications/85151128838

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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