@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",
}
