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arXiv · 2112.01925

Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study

Claire Little·Mark Elliot·Richard Allmendinger·Sahel Shariati Samani
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Abstract

Generative Adversarial Networks (GANs) are gaining increasing attention as a means for synthesising data. So far much of this work has been applied to use cases outside of the data confidentiality domain with a common application being the production of artificial images. Here we consider the potential application of GANs for the purpose of generating synthetic census microdata. We employ a battery of utility metrics and a disclosure risk metric (the Targeted Correct Attribution Probability) to compare the data produced by tabular GANs with those produced using orthodox data synthesis methods.

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Claire Little, Mark Elliot, Richard Allmendinger, Sahel Shariati Samani. 2021-12-03. Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study. https://arxiv.org/abs/2112.01925

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