# Differential Privacy in Cooperative Multiagent Planning

arXiv: https://arxiv.org/abs/2301.08811 · HTML: https://mlsanity.com/paper/2301.08811/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 9 of 9)

- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1905.05408/) (2019)
- [Differential Privacy: An Economic Method for Choosing Epsilon](https://mlsanity.com/paper/1402.3329/) (2014)
- [Local Differential Privacy for Regret Minimization in Reinforcement Learning](https://mlsanity.com/paper/2010.07778/) (2020)
- [Offline Reinforcement Learning with Differential Privacy](https://mlsanity.com/paper/2206.00810/) (2022)
- [Differential Privacy for Symbolic Systems with Application to Markov Chains](https://mlsanity.com/paper/2202.03325/) (2022)
- [Scalable Anytime Planning for Multi-Agent MDPs](https://mlsanity.com/paper/2101.04788/) (2021)
- [Differentially Private Multi-Agent Planning for Logistic-like Problems](https://mlsanity.com/paper/2008.06832/) (2020)
- [Planning Not to Talk: Multiagent Systems that are Robust to Communication Loss](https://mlsanity.com/paper/2201.06619/) (2022)

## Built on by (most cited first, 2 of 2)

- [Privacy-Enhancing Paradigms within Federated Multi-Agent Systems](https://mlsanity.com/paper/2503.08175/) (2025)
- [Deceptive Sequential Decision-Making via Regularized Policy Optimization](https://mlsanity.com/paper/2501.18803/) (2025)

## Abstract (arXiv)

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 then we 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 3 percent when comparing private versus non-private implementations of communication. By contrast, the team's performance decreases by roughly 86 percent when using baseline policies that ignore total correlation and only optimize team performance.