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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
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