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.

翻译：合成数据生成是一种在真实数据访问受限或隐私法规限制敏感数据可用性时的先进解决方案。目前已有相当数量的研究专注于单表格数据集的合成数据生成，但对于具有复杂表格关系的多表格数据集，相关研究仍较为有限。本文提出HCTGAN算法，用于从复杂的多表格数据集中合成多表格数据。我们将实验结果与概率模型HMA1进行了对比。研究结果表明，对于深度复杂的多表格数据集，我们提出的算法能够更高效地生成大规模合成数据，同时保证足够的数据质量并始终维持参照完整性。我们得出结论：HCTGAN算法适用于为具有复杂关系的深度多表格数据集高效生成大规模合成数据。此外我们建议，当数据质量成为首要考量时，HMA1模型更适合应用于规模较小的数据集。