# Synthetic Data: Methods, Use Cases, and Risks

arXiv: https://arxiv.org/abs/2303.01230 · HTML: https://mlsanity.com/paper/2303.01230/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Scalable Private Learning with PATE](https://mlsanity.com/paper/1802.08908/) (2018)
- [Differentially Private Generative Adversarial Network](https://mlsanity.com/paper/1802.06739/) (2018)
- [Synthetic Data -- Anonymisation Groundhog Day](https://mlsanity.com/paper/2011.07018/) (2020)
- [Knock Knock, Who's There? Membership Inference on Aggregate Location Data](https://mlsanity.com/paper/1708.06145/) (2017)
- [DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges](https://mlsanity.com/paper/2106.12949/) (2021)

## Built on by (most cited first, 10 of 10)

- [The DCR Delusion: Measuring the Privacy Risk of Synthetic Data](https://mlsanity.com/paper/2505.01524/) (2025)
- [A Systematic Review of Federated Generative Models](https://mlsanity.com/paper/2405.16682/) (2024)
- [The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data](https://mlsanity.com/paper/2504.06923/) (2025)
- [When Synthetic Data Met Regulation](https://mlsanity.com/paper/2307.00359/) (2023)
- [dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation](https://mlsanity.com/paper/2506.00322/) (2025)
- [The Pitfalls of "Security by Obscurity" And What They Mean for Transparent AI](https://mlsanity.com/paper/2501.18669/) (2025)
- [Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliance](https://mlsanity.com/paper/2407.16929/) (2024)
- [Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective](https://mlsanity.com/paper/2601.22434/) (2026)
- [Tight Auditing of Differential Privacy in MST and AIM](https://mlsanity.com/paper/2604.18352/) (2026)
- [Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy](https://mlsanity.com/paper/2504.08254/) (2025)

## Abstract (arXiv)

Sharing data can often enable compelling applications and analytics. However, more often than not, valuable datasets contain information of a sensitive nature, and thus, sharing them can endanger the privacy of users and organizations. A possible alternative gaining momentum in both the research community and industry is to share synthetic data instead. The idea is to release artificially generated datasets that resemble the actual data -- more precisely, having similar statistical properties. In this article, we provide a gentle introduction to synthetic data and discuss its use cases, the privacy challenges that are still unaddressed, and its inherent limitations as an effective privacy-enhancing technology.