  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.
