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arXiv · 2112.09238

Benchmarking Differentially Private Synthetic Data Generation Algorithms

Yuchao Tao·Ryan McKenna·Michael Hay·Ashwin Machanavajjhala·Gerome Miklau
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Abstract

This work presents a systematic benchmark of differentially private synthetic data generation algorithms that can generate tabular data. Utility of the synthetic data is evaluated by measuring whether the synthetic data preserve the distribution of individual and pairs of attributes, pairwise correlation as well as on the accuracy of an ML classification model. In a comprehensive empirical evaluation we identify the top performing algorithms and those that consistently fail to beat baseline approaches.

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Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau. 2021-12-16. Benchmarking Differentially Private Synthetic Data Generation Algorithms. https://arxiv.org/abs/2112.09238

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