mlsanityData to 7 Oct 2026

Synthetic Data: Methods, Use Cases, and Risks

arXiv 2303.01230 ↗First posted 1 Mar 2023cs.CR cs.AI cs.CYcited by 10

Not on the map. Why?

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 6 of its 25 references that are in the map. First posted on arXiv 1 Mar 2023.

Builds on

6 of 25 references.

References · 6

  1. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  2. 2018Scalable Private Learning with PATEcited by 266
  3. 2018Differentially Private Generative Adversarial Networkcited by 235
  4. 2020Synthetic Data -- Anonymisation Groundhog Daycited by 105
  5. 2017Knock Knock, Who's There? Membership Inference on Aggregate Location Datacited by 63
  6. 2021DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challengescited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 5; 2026: 2.

10 papers.

  1. 2025The DCR Delusion: Measuring the Privacy Risk of Synthetic Datacited by 11
  2. 2024A Systematic Review of Federated Generative Modelscited by 5
  3. 2025The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Datacited by 4
  4. 2023When Synthetic Data Met Regulationcited by 3
  5. 2025The Pitfalls of "Security by Obscurity" And What They Mean for Transparent AIcited by 2
  6. 2025dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generationcited by 2
  7. 2024Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliancecited by 1
  8. 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspectivecited by 1
  9. 2025Understanding the Impact of Data Domain Extraction on Synthetic Data Privacycited by 0
  10. 2026Tight Auditing of Differential Privacy in MST and AIMcited by 0