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Differentially Private LQ Control
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AI
DOI：10.1109/TAC.2022.3148710.png)
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Abstract
En 中文
As multi-agent systems proliferate and more user data, new approaches are needed to protect sensitive data while still enabling system operation. To address this need, this article presents a private multiagent LQ control framework. Agents' state trajectories can be sensitive, and we therefore protect them using differential privacy. We quantify the impact of privacy along three dimensions: the amount of information shared under privacy, the control-theoretic cost of privacy, and the tradeoffs between privacy and performance. These analyses are done in conventional control-theoretic terms, which we use to develop guidelines for calibrating privacy as a function of system parameters. Numerical results indicate that system performance remains within desirable ranges, even under strict privacy requirements.
Keywords:
Privacy
Differential privacy
Trajectory
Costs
Power system stability
Standards
Guidelines
multiagent systems
network control
privacy
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
