mlsanityData to 14 Oct 2026

Winning the NIST Contest: A scalable and general approach to differentially private synthetic data

arXiv 2108.04978 ↗First posted 11 Aug 2021cs.CRcited by 102

Not on the map. Why?

Abstract (arXiv)

We propose a general approach for differentially private synthetic data generation, that consists of three steps: (1) select a collection of low-dimensional marginals, (2) measure those marginals with a noise addition mechanism, and (3) generate synthetic data that preserves the measured marginals well. Central to this approach is Private-PGM, a post-processing method that is used to estimate a high-dimensional data distribution from noisy measurements of its marginals. We present two mechanisms, NIST-MST and MST, that are instances of this general approach. NIST-MST was the winning mechanism in the 2018 NIST differential privacy synthetic data competition, and MST is a new mechanism that can work in more general settings, while still performing comparably to NIST-MST. We believe our general approach should be of broad interest, and can be adopted in future mechanisms for synthetic data generation.

Key facts. Cited by 102 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Aug 2021.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 11; 2023: 19; 2024: 27; 2025: 18; 2026: 24.

102 papers.

Sort:

By area:

  1. 2023How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacycited by 153
  2. 2022Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipecited by 108
  3. 2022Synthetic Data -- what, why and how?cited by 67
  4. 2021Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Datacited by 48
  5. 2023SoK: Privacy-Preserving Data Synthesiscited by 38
  6. 2024Privacy-Preserving Instructions for Aligning Large Language Modelscited by 35
  7. 2023A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Datacited by 24
  8. 2024Systematic Assessment of Tabular Data Synthesiscited by 18
  9. 2024PANORAMIA: Privacy Auditing of Machine Learning Models without Retrainingcited by 17
  10. 2023The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasetscited by 14
  11. 2023Auditing and Generating Synthetic Data with Controllable Trust Trade-offscited by 13
  12. 2023Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utilitycited by 13
38 more
  1. 2023CuTS: Customizable Tabular Synthetic Data Generationcited by 13
  2. 2023Unified Enhancement of Privacy Bounds for Mixture Mechanisms via f-Differential Privacycited by 13
  3. 2024The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debuggingcited by 13
  4. 2022dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generationcited by 12
  5. 2025The DCR Delusion: Measuring the Privacy Risk of Synthetic Datacited by 11
  6. 2024Privacy Vulnerabilities in Marginals-based Synthetic Datacited by 10
  7. 2022Evaluating the Fairness Impact of Differentially Private Synthetic Datacited by 9
  8. 2022Noise-Aware Statistical Inference with Differentially Private Synthetic Datacited by 9
  9. 2023Post-processing Private Synthetic Data for Improving Utility on Selected Measurescited by 9
  10. 2025Synthetic Tabular Data: Methods, Attacks and Defensescited by 9
  11. 2022On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulationcited by 8
  12. 2023Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Datacited by 8
  13. 2024Privacy-preserving data release leveraging optimal transport and particle gradient descentcited by 8
  14. 2024Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Datacited by 8
  15. 2024Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learningcited by 8
  16. 2023Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithmscited by 7
  17. 2021Relaxed Marginal Consistency for Differentially Private Query Answeringcited by 6
  18. 2022Secure Multiparty Computation for Synthetic Data Generation from Distributed Datacited by 6
  19. 2023Diverse Community Data for Benchmarking Data Privacy Algorithmscited by 6
  20. 2024Tabular Data Synthesis with Differential Privacy: A Surveycited by 6
  21. 2022Private Quantiles Estimation in the Presence of Atomscited by 5
  22. 2023Privately generating tabular data using language modelscited by 5
  23. 2024DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generatorscited by 5
  24. 2025Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?cited by 5
  25. 2025The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Datacited by 5
  26. 2023Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directionscited by 4
  27. 2023A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacycited by 4
  28. 2024Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?cited by 4
  29. 2024Private Regression via Data-Dependent Sufficient Statistic Perturbationcited by 4
  30. 2025Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inferencecited by 4
  31. 2024Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Datacited by 3
  32. 2025dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generationcited by 3
  33. 2022NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Releasecited by 2
  34. 2022Spending Privacy Budget Fairly and Wiselycited by 2
  35. 2022Answering Private Linear Queries Adaptively using the Common Mechanismcited by 2
  36. 2024An Algorithm for Streaming Differentially Private Datacited by 2
  37. 2024A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasetscited by 2
  38. 2024Differentially Private Synthetic Data with Private Density Estimationcited by 2

50 most cited of 102 All 102 (JSON)