TITLE: Differentially Private Algorithms for Synthetic Power System Datasets 

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.
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.
Differentially Private Algorithms for Synthetic Power System Datasets
http://arxiv.org/abs/2303.11079v1