# To democratize research with sensitive data, we should make synthetic data more accessible

arXiv: https://arxiv.org/abs/2404.17271 · HTML: https://mlsanity.com/paper/2404.17271/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2024.

## Built on by (most cited first, 2 of 2)

- [Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees](https://mlsanity.com/paper/2510.16974/) (2025)
- [A Statistical Approach for Synthetic EEG Data Generation](https://mlsanity.com/paper/2504.16143/) (2025)

## Abstract (arXiv)

For over 30 years, synthetic data has been heralded as a promising solution to make sensitive datasets accessible. However, despite much research effort and several high-profile use-cases, the widespread adoption of synthetic data as a tool for open, accessible, reproducible research with sensitive data is still a distant dream. In this opinion, Erik-Jan van Kesteren, head of the ODISSEI Social Data Science team, argues that in order to progress towards widespread adoption of synthetic data as a privacy enhancing technology, the data science research community should shift focus away from developing better synthesis methods: instead, it should develop accessible tools, educate peers, and publish small-scale case studies.