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 | |
| 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 |
Keywords
- Nash Q-learning
- Privacy mechanism
- stochastic game
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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