Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study

• URL: http://arxiv.org/abs/2112.01925v1
• Date: Fri, 3 Dec 2021 14:23:17 GMT
• Title: Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study
• Authors: Claire Little, Mark Elliot, Richard Allmendinger, Sahel Shariati Samani
• Abstract summary: Generative Adversarial Networks (GANs) are gaining increasing attention as a means for synthesising data. Here we consider the potential application of GANs for the purpose of generating synthetic census microdata.
• Score: 1.0896567381206714
• License: http://creativecommons.org/licenses/by/4.0/
• 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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