Differentially Private Algorithms for Synthetic Power System Datasets

While power systems research relies on the availability of real-world network datasets, data owners (e.g., system operators) are hesitant to share data due to privacy risks. To control these risks, we develop privacy-preserving algorithms for synthetic generation of datasets for optimization and machine learning. Taking a real-world dataset as input, the algorithms output its noisy, synthetic version, which preserves the accuracy of the real data on a specific downstream model or even a large population of those. We control the privacy loss using Laplace and Exponential mechanisms of differential privacy and preserve data accuracy using a post-processing convex (or mixed-integer) optimization. We apply the algorithms to generate synthetic power network parameters and wind power data.