Abstract 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, we present a private multi-agent LQ control framework. We consider problems in which each agent has linear dynamics and the agents are coupled by a quadratic cost. Generating optimal control values for agents is a centralized operation, and we therefore introduce a cloud computer into the network for this purpose. The cloud is tasked with aggregating agents’ outputs, computing control inputs, and transmitting these inputs to the agents, which apply them in their state updates. Agents’ state trajectories can be sensitive and we therefore protect them using differential privacy. Differential privacy is a statistical notion of privacy enforced by adding noise to sensitive data (or functions thereof) before sharing it, and agents add noise to all data before sending it to the cloud. The result is a private multi-agent LQG framework in which agents’ states are protected from both the cloud and other agents. 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 are provided to illustrate these results.