Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study

2021/12/03 by Claire Little, Little, Claire, Mark Elliot +5 · 2 citations
Computer Science · #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning in Healthcare

paper · pdf · doi:10.48550/arxiv.2112.01925

openalex publication_date 2021/12/03 · openalex created_date 2021/12/31 · openalex updated_date 2026/07/28

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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