Differentially Private Algorithms for Synthetic Power System Datasets

• URL: http://arxiv.org/abs/2303.11079v1
• Date: Mon, 20 Mar 2023 13:38:58 GMT
• Title: Differentially Private Algorithms for Synthetic Power System Datasets
• Authors: Vladimir Dvorkin and Audun Botterud
• Abstract summary: Power systems research relies on the availability of real-world network datasets. Data owners are hesitant to share data due to security and privacy risks. We develop privacy-preserving algorithms for the synthetic generation of optimization and machine learning datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 security and privacy risks. To control these risks, we develop privacy-preserving algorithms for the synthetic generation of optimization and machine learning datasets. 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 optimization. We apply the algorithms to generate synthetic network parameters and wind power data.

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