Di-PriDA: Differentially Private Distributed Load Balancing Control for the Smart Grid

The future electrical grid, i.e., smart grid, is envisioned to use appliance-level control to provide sustainable power usage and flexible energy utilization. However, load trace monitoring for appliance-level control poses privacy concerns given that private behaviors can be inferred by electricity utilization levels. In this paper, we introduce a privacy-preserving and fine-grained power load data analysis mechanism Di-PriDA for appliance-level peak-time load balancing control in the smart grid. Di-PriDA achieved 3ε-differential privacy, which provided indistinguishable application power consumption data to protect against eavesdroppers. The proposed technique explores a new differential privacy problem: the distributed top-k problem without a trusted third party, and provides both rigorous provable privacy and an accuracy guarantee based on distributed differential privacy. We implement a prototype of Di-PriDA on an external microcontroller device used for smart meters, and evaluate its performance under two real-world power usage datasets, as well as a synthetic dataset through a combination of experiments and simulations. We find that Di-PriDA effectively achieves confidentiality for the appliance-level peak-time load balancing control while guaranteeing a good quality-of-service to the various stakeholders of the power grid.