Permutation-Invariant Tabular Data Synthesis

TL;DR

This paper investigates the permutation sensitivity of AI-based tabular data synthesizers and proposes solutions to improve permutation invariance, resulting in higher quality and utility of synthetic data for privacy-preserving data sharing.

Contribution

It introduces AE-GAN and a feature sorting algorithm to address permutation sensitivity in tabular data synthesis, enhancing data quality and utility.

Findings

Permutation sensitivity can reduce data quality by up to 38.67%.

Proposed solutions improve permutation invariance and data utility by up to 22%.

Enhanced synthetic data quality benefits downstream data analysis tasks.

Abstract

Tabular data synthesis is an emerging approach to circumvent strict regulations on data privacy while discovering knowledge through big data. Although state-of-the-art AI-based tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, their training is sensitive to column permutations of input data. In this paper, we first conduct an extensive empirical study to disclose such a property of permutation invariance and an in-depth analysis of the existing synthesizers. We show that changing the input column order worsens the statistical difference between real and synthetic data by up to 38.67% due to the encoding of tabular data and the network architectures. To fully unleash the potential of big synthetic tabular data, we propose two solutions: (i) AE-GAN, a synthesizer that uses an autoencoder network to represent the tabular data and GAN networks to synthesize the latent representation, and (ii) a feature sorting algorithm to find the suitable column order of input data for CNN-based synthesizers. We evaluate the proposed solutions on five datasets in terms of the sensitivity to the column permutation, the quality of synthetic data, and the utility in downstream analyses. Our results show that we enhance the property of permutation-invariance when training synthesizers and further improve the quality and utility of synthetic data, up to 22%, compared to the existing synthesizers.