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Benchmarking Differentially Private Synthetic Data Generation Algorithms

arXiv 2112.09238 ↗First posted 16 Dec 2021cs.CRcited by 80

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Abstract (arXiv)

This work presents a systematic benchmark of differentially private synthetic data generation algorithms that can generate tabular data. Utility of the synthetic data is evaluated by measuring whether the synthetic data preserve the distribution of individual and pairs of attributes, pairwise correlation as well as on the accuracy of an ML classification model. In a comprehensive empirical evaluation we identify the top performing algorithms and those that consistently fail to beat baseline approaches.

Key facts. Cited by 80 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Dec 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 12; 2023: 21; 2024: 18; 2025: 17; 2026: 11.

80 papers.

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  1. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  2. 2023How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacycited by 150
  3. 2022Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipecited by 108
  4. 2022Synthetic Data -- what, why and how?cited by 66
  5. 2023Harnessing large-language models to generate private synthetic textcited by 65
  6. 2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
  7. 2022Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluationcited by 39
  8. 2023SoK: Privacy-Preserving Data Synthesiscited by 37
  9. 2022Private Synthetic Data for Multitask Learning and Marginal Queriescited by 35
  10. 2024Privacy-Preserving Instructions for Aligning Large Language Modelscited by 34
  11. 2023A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Datacited by 23
  12. 2024Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey)cited by 20
38 more
  1. 2023Generating Private Synthetic Data with Genetic Algorithmscited by 18
  2. 2023Private GANs, Revisitedcited by 17
  3. 2023The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasetscited by 14
  4. 2023Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utilitycited by 13
  5. 2023DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generationcited by 13
  6. 2025Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Modelcited by 13
  7. 2022dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generationcited by 12
  8. 2023CuTS: Customizable Tabular Synthetic Data Generationcited by 12
  9. 2024The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debuggingcited by 12
  10. 2025DPImageBench: A Unified Benchmark for Differentially Private Image Synthesiscited by 11
  11. 2024Discriminative Estimation of Total Variation Distance: A Fidelity Auditor for Generative Datacited by 10
  12. 2024Privacy Vulnerabilities in Marginals-based Synthetic Datacited by 10
  13. 2025Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectivescited by 10
  14. 2022Noise-Aware Statistical Inference with Differentially Private Synthetic Datacited by 9
  15. 2023Post-processing Private Synthetic Data for Improving Utility on Selected Measurescited by 9
  16. 2022On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulationcited by 8
  17. 2023Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Datacited by 8
  18. 2024Privacy-preserving data release leveraging optimal transport and particle gradient descentcited by 8
  19. 2025Synthetic Tabular Data: Methods, Attacks and Defensescited by 8
  20. 2025Struct-Bench: A Benchmark for Differentially Private Structured Text Generationcited by 7
  21. 2023Diverse Community Data for Benchmarking Data Privacy Algorithmscited by 6
  22. 2023FLAIM: AIM-based Synthetic Data Generation in the Federated Settingcited by 6
  23. 2024Synthetic Data: Revisiting the Privacy-Utility Trade-offcited by 6
  24. 2024Differential Privacy Under Class Imbalance: Methods and Empirical Insightscited by 6
  25. 2023Privately generating tabular data using language modelscited by 5
  26. 2023The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Datacited by 5
  27. 2024DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generatorscited by 5
  28. 2025Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?cited by 5
  29. 2022Decouple-and-Sample: Protecting sensitive information in task agnostic data releasecited by 4
  30. 2022Provable Membership Inference Privacycited by 4
  31. 2023A Unified View of Differentially Private Deep Generative Modelingcited by 4
  32. 2023A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacycited by 4
  33. 2024Private Regression via Data-Dependent Sufficient Statistic Perturbationcited by 4
  34. 2025The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Datacited by 4
  35. 2024Synthetic Data Outliers: Navigating Identity Disclosurecited by 3
  36. 2022Spending Privacy Budget Fairly and Wiselycited by 2
  37. 2024An Algorithm for Streaming Differentially Private Datacited by 2
  38. 2024Differentially Private Synthetic Data Generation for Relational Databasescited by 2

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