2023-07-02-chen23e.md

abstract	openreview	title	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Privacy-aware multiagent systems must protect agents’ sensitive data while simultaneously ensuring that agents accomplish their shared objectives. Towards this goal, we propose a framework to privatize inter-agent communications in cooperative multiagent decision-making problems. We study sequential decision-making problems formulated as cooperative Markov games with reach-avoid objectives. We apply a differential privacy mechanism to privatize agents’ communicated symbolic state trajectories, and analyze tradeoffs between the strength of privacy and the team’s performance. For a given level of privacy, this tradeoff is shown to depend critically upon the total correlation among agents’ state-action processes. We synthesize policies that are robust to privacy by reducing the value of the total correlation. Numerical experiments demonstrate that the team’s performance under these policies decreases by only 6 percent when comparing private versus non-private implementations of communication. By contrast, the team’s performance decreases by 88 percent when using baseline policies that ignore total correlation and only optimize team performance.	0hX6PNo0aVD	Differential Privacy in Cooperative Multiagent Planning	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	chen23e	0	Differential Privacy in Cooperative Multiagent Planning	347	357	347-357	347	false	Chen, Bo and Hawkins, Calvin and Karabag, Mustafa O. and Neary, Cyrus and Hale, Matthew and Topcu, Ufuk	given family Bo Chen given family Calvin Hawkins given family Mustafa O. Karabag given family Cyrus Neary given family Matthew Hale given family Ufuk Topcu	2023-07-02		Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence	216	inproceedings	date-parts 2023 7 2	https://proceedings.mlr.press/v216/chen23e/chen23e.pdf	label link Supplementary PDF https://proceedings.mlr.press/v216/chen23e/chen23e-supp.pdf