Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.
Original languageEnglish
Title of host publication2021 Expert Meeting on Statistical Data Confidentiality
Publication statusAccepted/In press - 16 Nov 2021

Research Beacons, Institutes and Platforms

  • Cathie Marsh Institute


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