Work

Preserving logical and functional dependencies in synthetic tabular data

Year: 2025

Type: article

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. • We introduced the notion of logical dependencies in tabular data. • We introduce a novel Bayesian measure to identify logical dependencies. • State-of-the-art models are not able to preserve functional dependencies. • Our results show that state-of-the-art algorithms can preserve logical dependencies.

Source: Pattern Recognition

DOI: 10.1016/j.patcog.2025.111459

Authors: Chaithra Umesh, Kristian Schultz, Manjunath Mahendra, Saptarshi Bej, Olaf Wolkenhauer

Institutions: University of Rostock, Indian Institute of Science Education and Research Thiruvananthapuram, Stellenbosch University, Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Technical University of Munich

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,590

Funder: Deutsche Forschungsgemeinschaft