Differentially Private System for Residential Energy Management via Markov Decision Process

With the development of smart grid, smart meters play an important role during the process of data collection and delivery, where users’ electricity consumption information may be exposed to malicious users. The attacks to these sensitive information may touch off serious privacy concerns and pose a threat to the security of power system. In this paper, we design a novel framework to satisfy both personalized privacy preservation and customized system cost in the smart metering system, which contains a Rechargeable Battery (RB). First, we adopt differential privacy algorithm to enhance privacy and define the privacy preservation level for the privacy measure. Second, we specially design a comprehensive cost computing system, including electricity cost, battery operation cost, and the privacy budget. Then Markov Decision Process (MDP) is utilized in this work to formulate an optimization problem figuring out the trade-off between privacy and cost. Deploying Sarsa algorithm to address the optimization problem, we can finally obtain an optimal energy management system for different customers according to their personalized demands. Experimental evaluation results show that our system is able to balance privacy protection and cost saving with the introduction of RB.