Survey on Tabular Data Privacy and Synthetic Data Generation in Industry 4.0

Abstract

Synthetic data is an emerging field that solves the raised need for privacy-preserving data sharing and the lack of real data. One of the most common data types used is tabular data, which is widely used to train machine learning models, especially in the industrial domain for better decision-making and edge case handling, two key points in Industry 4.0. In this paper, we present and evaluate state-of-the-art models for tabular data generation under a proposed taxonomy consisting of statistical models, generative adversarial networks (GANs)-based models, denoising diffusion probabilistic models (DDPMs), and large language models (LLMs). Additionally, we propose a revised evaluation taxonomy consisting of three dimensions, including realism, representativeness, and privacy. The results proved that analyzing models based on multiple metrics from each category could ensure a better understanding of the dataset when used for downstream tasks. Finally, we found that models based on GANs are still a solid option in multiple cases, such as a constrained computational environment. In contrast, models based on LLMs and DDPMs are more promising in terms of realism and representativeness. More research should be invested in overcoming limitations such as numerical data representation and long training times for LLMs. Our survey serves as a study for existing models and newer directions in the field, with guidelines for evaluation that can be applied to industrial and other domains.