# Advancing Microdata Privacy Protection: A Review of Synthetic Data

arXiv: https://arxiv.org/abs/2308.00872 · HTML: https://mlsanity.com/paper/2308.00872/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2023.

## Built on by (most cited first, 1 of 1)

- [Developing synthetic microdata through machine learning for firm-level business surveys](https://mlsanity.com/paper/2512.05948/) (2025)

## Abstract (arXiv)

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.