IRG: Generating Synthetic Relational Databases using GANs
Jiayu Li Y. C. Tay
There is an overgrowing demand for data sharing in academia and industry.
However, such sharing has issues with personal privacy and data
confidentiality. One option is to share only synthetically-generated versions
of the real data. Generative Adversarial Network (GAN) is a recently-popular
technique that can be used for this purpose.
  Relational databases usually have multiple tables that are related to each
other. So far, the use of GANs has essentially focused on generating single
tables. This paper presents Incremental Relational Generator (IRG), which uses
GANs to synthetically generate interrelated tables. Given an empirical
relational database, IRG can generate a synthetic version that can be safely
shared.
  IRG generates the tables in some sequential order. The key idea is to
construct a context, based on the tables generated so far, when using a GAN to
generate the next table. Experiments with public datasets and private student
data show that IRG outperforms state-of-the-art in terms of statistical
properties and query results.