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Differentially Private LQ Control. (arXiv:1807.05082v1 [math.OC])

来源于:arXiv
As multi-agent systems proliferate and share more and more user data, new approaches are needed to protect sensitive data while still guaranteeing successful 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 the 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 information can be sensitive and we therefore protect it using differential privacy. Differential privacy is a statistical notion of privacy enforced by adding noise to sensitive data before sharing it, and agents will therefore add noise to all data before sending it to the cloud. The result is a private mu 查看全文>>