mlsanityData to 7 Oct 2026

When Synthetic Data Met Regulation

arXiv 2307.00359 ↗First posted 1 Jul 2023cs.LG cs.AI cs.CRcited by 3

Not on the map. Why?

Abstract (arXiv)

In this paper, we argue that synthetic data produced by Differentially Private generative models can be sufficiently anonymized and, therefore, anonymous data and regulatory compliant.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 24 of its 46 references that are in the map. First posted on arXiv 1 Jul 2023.

Builds on

24 of 46 references.

References · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2020Extracting Training Data from Large Language Modelscited by 2,144
  5. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  6. 2023Extracting Training Data from Diffusion Modelscited by 723
  7. 2019Does Learning Require Memorization? A Short Tale about a Long Tailcited by 452
  8. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 373
  9. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 347
  10. 2022Language Models are Realistic Tabular Data Generatorscited by 290
  11. 2018Scalable Private Learning with PATEcited by 266
  12. 2021Adversary Instantiation: Lower Bounds for Differentially Private Machine Learningcited by 193
12 more
  1. 2022Differentially Private Diffusion Modelscited by 118
  2. 2023REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformerscited by 93
  3. 2023Differentially Private Diffusion Models Generate Useful Synthetic Imagescited by 85
  4. 2021On Memorization in Probabilistic Deep Generative Modelscited by 60
  5. 2021Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Datacited by 48
  6. 2022A Unified Framework for Quantifying Privacy Risk in Synthetic Datacited by 45
  7. 2014Differential Privacy: An Economic Method for Choosing Epsiloncited by 34
  8. 2019Towards Formalizing the GDPR's Notion of Singling Outcited by 25
  9. 2023Arbitrary Decisions are a Hidden Cost of Differentially Private Trainingcited by 19
  10. 2021"I need a better description'': An Investigation Into User Expectations For Differential Privacycited by 17
  11. 2023Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utilitycited by 13
  12. 2023Synthetic Data: Methods, Use Cases, and Riskscited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2023On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprisecited by 1
  2. 2024Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliancecited by 1
  3. 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspectivecited by 1