Masking Method for Local Information on Distributed Optimization with Constraints
Kazuma Wada, Kazunori Sakurama · 2016
We propose a masking method to protect agents' privacy for distributed optimization. In the proposed method, Agents add some signals to the own original state to conceal private information. To obtain the correct solution of the optimization problem, they exchange the added signals and subtract the received signals from the own state. Finally, we apply it to a microgrid and show that the supply-demand balance is kept via real-time pricing while protecting privacy of agent.