LAUSR

Smart grids rely on communication networks to connect the physical devices and the control and computation technologies. The transmission of sensitive data over the network induces the possibility of leakage of private and sensitive information about various entities and components in the grid. To address this issue, this article proposes a privacy-preserving framework to enhance the privacy of smart grids integrated with software-defined networks. The proposed framework uses two privacy metrics (mutual information and differential privacy) and formulates a privacy-preserving distributed optimization algorithm with the objective of minimizing the network cost. We view the distributed optimization algorithm as an $n$ -player, noncooperative game and provide distributed techniques to solve the optimization problem efficiently. We prove that our algorithm converges to the Nash equilibrium of the game while preserving the data privacy. We validate the performance of our approach using three IEEE bus systems and realistic Internet service provider network topology.