Advancing Microdata Privacy Protection: A Review of Synthetic Data

TL;DR

This paper reviews the development, techniques, challenges, and applications of synthetic data generation for privacy protection, emphasizing its importance and future prospects in safeguarding record-level data.

Contribution

It offers a comprehensive overview of synthetic data generation methods, evaluation techniques, and discusses current challenges and future research directions in privacy protection.

Findings

Synthetic data effectively protects privacy in data sharing.

Current methods face challenges in data utility and realism.

Future research needed for improved synthetic data techniques.

Abstract

Synthetic data generation is a powerful tool for privacy protection when considering public release of record-level data files. Initially proposed about three decades ago, it has generated significant research and application interest. To meet the pressing demand of data privacy protection in a variety of contexts, the field needs more researchers and practitioners. This review provides a comprehensive introduction to synthetic data, including technical details of their generation and evaluation. Our review also addresses the challenges and limitations of synthetic data, discusses practical applications, and provides thoughts for future work.