Hierarchical Conditional Tabular GAN for Multi-Tabular Synthetic Data Generation

TL;DR

This paper introduces HCTGAN, a novel hierarchical GAN model designed to efficiently generate large-scale, high-quality synthetic multi-tabular data with complex relationships, while ensuring referential integrity.

Contribution

The paper presents HCTGAN, a new hierarchical GAN architecture tailored for multi-tabular data, outperforming existing models like HMA1 in scalability and maintaining data integrity.

Findings

HCTGAN efficiently generates large synthetic datasets.

HCTGAN maintains referential integrity in complex data.

HCTGAN achieves comparable data quality to existing models.

Abstract

The generation of synthetic data is a state-of-the-art approach to leverage when access to real data is limited or privacy regulations limit the usability of sensitive data. A fair amount of research has been conducted on synthetic data generation for single-tabular datasets, but only a limited amount of research has been conducted on multi-tabular datasets with complex table relationships. In this paper we propose the algorithm HCTGAN to synthesize multi-tabular data from complex multi-tabular datasets. We compare our results to the probabilistic model HMA1. Our findings show that our proposed algorithm can more efficiently sample large amounts of synthetic data for deep and complex multi-tabular datasets, whilst achieving adequate data quality and always guaranteeing referential integrity. We conclude that the HCTGAN algorithm is suitable for generating large amounts of synthetic data efficiently for deep multi-tabular datasets with complex relationships. We additionally suggest that the HMA1 model should be used on smaller datasets when emphasis is on data quality.