Preserving logical and functional dependencies in synthetic tabular data

TL;DR

This paper investigates whether current synthetic tabular data generation methods can preserve logical and functional dependencies among attributes, introduces a measure for logical dependencies, and compares algorithms' effectiveness in maintaining these dependencies.

Contribution

It introduces the concept of logical dependencies, proposes a measure for them, and evaluates how well existing algorithms preserve these and functional dependencies in synthetic data.

Findings

Current algorithms often fail to preserve functional dependencies.

Some models can preserve inter-attribute logical dependencies.

Research gaps identified for developing task-specific synthetic data models.

Abstract

Dependencies among attributes are a common aspect of tabular data. However, whether existing tabular data generation algorithms preserve these dependencies while generating synthetic data is yet to be explored. In addition to the existing notion of functional dependencies, we introduce the notion of logical dependencies among the attributes in this article. Moreover, we provide a measure to quantify logical dependencies among attributes in tabular data. Utilizing this measure, we compare several state-of-the-art synthetic data generation algorithms and test their capability to preserve logical and functional dependencies on several publicly available datasets. We demonstrate that currently available synthetic tabular data generation algorithms do not fully preserve functional dependencies when they generate synthetic datasets. In addition, we also showed that some tabular synthetic data generation models can preserve inter-attribute logical dependencies. Our review and comparison of the state-of-the-art reveal research needs and opportunities to develop task-specific synthetic tabular data generation models.