Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis
This is an incremental review paper that synthesizes existing knowledge for researchers and practitioners working with synthetic data generation in privacy-sensitive domains.
This paper provides a comprehensive review of deep generative models for generating synthetic tabular data, focusing on their application to privacy-sensitive datasets. It synthesizes recent developments, advantages over other methods, and key challenges like data normalization and privacy concerns.
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.