Towards Secure Metering Data Analysis via Distributed Differential Privacy

The future electrical grid, i.e., smart grid, will utilize appliance-level control to provide sustainable power usage and flexible energy utilization. However, load trace monitoring for appliance-level control poses privacy concerns with inferring private information. In this paper, we introduce a privacy-preserving and fine-grained power load data analysis mechanism for appliance-level peak-time load balance control in the smart grid. The proposed technique provides rigorous provable privacy and an accuracy guarantee based on distributed differential privacy. We simulate the scheme as privacy modules in the smart meter and the concentrator, and evaluate its performance under a real-world power usage dataset, which validates the efficiency and accuracy of the proposed scheme.