# Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods

arXiv: https://arxiv.org/abs/2012.04580 · HTML: https://mlsanity.com/paper/2012.04580/

## Key facts

- Cited by 13 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 13 of 13)

- [Brain Imaging Generation with Latent Diffusion Models](https://mlsanity.com/paper/2209.07162/) (2022)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications](https://mlsanity.com/paper/2112.12047/) (2021)
- [Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models](https://mlsanity.com/paper/2306.01322/) (2023)
- [A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources](https://mlsanity.com/paper/2203.07018/) (2022)
- [What Does DALL-E 2 Know About Radiology?](https://mlsanity.com/paper/2209.13696/) (2022)
- [Generating tabular datasets under differential privacy](https://mlsanity.com/paper/2308.14784/) (2023)
- [Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention](https://mlsanity.com/paper/2305.03710/) (2023)
- [Synthetic Data for Feature Selection](https://mlsanity.com/paper/2211.03035/) (2022)
- [A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters](https://mlsanity.com/paper/2211.09706/) (2022)
- [PRO: Projection Domain Synthesis for CT Imaging](https://mlsanity.com/paper/2506.13443/) (2025)
- [Interpretable machine learning for high-dimensional trajectories of aging health](https://mlsanity.com/paper/2105.03410/) (2021)
- [Synthetic Data Generator for Adaptive Interventions in Global Health](https://mlsanity.com/paper/2303.01954/) (2023)

## Abstract (arXiv)

Many ground-breaking advancements in machine learning can be attributed to the availability of a large volume of rich data. Unfortunately, many large-scale datasets are highly sensitive, such as healthcare data, and are not widely available to the machine learning community. Generating synthetic data with privacy guarantees provides one such solution, allowing meaningful research to be carried out "at scale" - by allowing the entirety of the machine learning community to potentially accelerate progress within a given field. In this article, we provide a high-level view of synthetic data: what it means, how we might evaluate it and how we might use it.