Design of Privacy Mechanism for Cyber Physical Systems: A Nash Q-learning Approach
| Title | Design of Privacy Mechanism for Cyber Physical Systems: A Nash Q-learning Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Zhang, Qirui, Meng, Siqi, Liu, Kun, Dai, Wei |
| Conference Name | 2022 China Automation Congress (CAC) |
| Keywords | control theory, Costs, CPS, Games, Human Behavior, human factors, measurement uncertainty, Nash Q-learning, numerical simulation, privacy, Privacy mechanism, pubcrawl, q-learning, resilience, Resiliency, Scalability, Stochastic game, Stochastic processes |
| 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. |
| Notes | ISSN: 2688-0938 |
| DOI | 10.1109/CAC57257.2022.10054655 |
| Citation Key | zhang_design_2022 |