Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis

TL;DR

This paper reviews recent advances in using deep generative models to create synthetic tabular data, emphasizing privacy preservation and discussing challenges, advantages, and evaluation methods for such models.

Contribution

It provides a comprehensive synthesis of deep generative models for synthetic tabular data, highlighting their advantages and addressing privacy and evaluation challenges.

Findings

Deep generative models effectively generate privacy-preserving synthetic tabular data.

Challenges include data normalization, privacy concerns, and model evaluation.

Deep models outperform traditional methods in synthetic data quality.

Abstract

This article provides a comprehensive synthesis of the recent developments in synthetic data generation via deep generative models, focusing on tabular datasets. We specifically outline the importance of synthetic data generation in the context of privacy-sensitive data. Additionally, we highlight the advantages of using deep generative models over other methods and provide a detailed explanation of the underlying concepts, including unsupervised learning, neural networks, and generative models. The paper covers the challenges and considerations involved in using deep generative models for tabular datasets, such as data normalization, privacy concerns, and model evaluation. This review provides a valuable resource for researchers and practitioners interested in synthetic data generation and its applications.