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.
@article{arxiv.2112.01925,
title = {Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study},
author = {Claire Little and Mark Elliot and Richard Allmendinger and Sahel Shariati Samani},
journal= {arXiv preprint arXiv:2112.01925},
year = {2021}
}