Differentially Private LQ Control
This addresses privacy concerns in multi-agent systems for users and operators, but it is incremental as it applies existing differential privacy to a specific control framework.
The paper tackled the problem of protecting sensitive state trajectories in multi-agent LQ control systems using differential privacy, and it quantified the impact on information sharing, control cost, and performance trade-offs, with numerical results showing performance remains within desirable ranges under strict privacy.
As multi-agent systems proliferate and share more user data, new approaches are needed to protect sensitive data while still enabling system operation. To address this need, this paper presents a private multi-agent 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.