mlsanityData to 14 Oct 2026

Synthetic Tabular Data: Methods, Attacks and Defenses

arXiv 2506.06108 ↗First posted 6 Jun 2025cs.LG cs.CRcited by 9

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Abstract (arXiv)

Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns. There has been much progress in synthetic data generation over the last decade, leveraging corresponding advances in machine learning and data analytics. In this survey, we cover the key developments and the main concepts in tabular synthetic data generation, including paradigms based on probabilistic graphical models and on deep learning. We provide background and motivation, before giving a technical deep-dive into the methodologies. We also address the limitations of synthetic data, by studying attacks that seek to retrieve information about the original sensitive data. Finally, we present extensions and open problems in this area.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 48 of its 76 references that are in the map. First posted on arXiv 6 Jun 2025.

Builds on

48 of 76 references.

References · 48

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2016Deep Learning with Differential Privacycited by 2,728
  6. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  7. 2021Membership Inference Attacks From First Principlescited by 878
  8. 2019Modeling Tabular data using Conditional GANcited by 699
  9. 2023A Comprehensive Overview of Large Language Modelscited by 648
  10. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 377
  11. 2016Concentrated Differential Privacy: Simplifications, Extensions, and Lower Boundscited by 346
  12. 2013The Composition Theorem for Differential Privacycited by 297
36 more
  1. 2022Language Models are Realistic Tabular Data Generatorscited by 294
  2. 2021Enhanced Membership Inference Attacks against Machine Learning Modelscited by 248
  3. 2023Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Spacecited by 186
  4. 2021On the Importance of Difficulty Calibration in Membership Inference Attackscited by 147
  5. 2019Reconstruction and Membership Inference Attacks against Generative Modelscited by 122
  6. 2020Synthetic Data -- Anonymisation Groundhog Daycited by 108
  7. 2023Low-Cost High-Power Membership Inference Attackscited by 107
  8. 2021Winning the NIST Contest: A scalable and general approach to differentially private synthetic datacited by 102
  9. 2022AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Datacited by 90
  10. 2021Benchmarking Differentially Private Synthetic Data Generation Algorithmscited by 81
  11. 2020DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generationcited by 78
  12. 2023Synthcity: facilitating innovative use cases of synthetic data in different data modalitiescited by 76
  13. 2019Graphical-model based estimation and inference for differential privacycited by 74
  14. 2023Membership Inference Attacks against Synthetic Data through Overfitting Detectioncited by 66
  15. 2022TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Datacited by 57
  16. 2021Differentially Private Query Release Through Adaptive Projectioncited by 52
  17. 2023RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Modelcited by 47
  18. 2021Leveraging Public Data for Practical Private Query Releasecited by 39
  19. 2022Private Synthetic Data for Multitask Learning and Marginal Queriescited by 35
  20. 2022Private Estimation with Public Datacited by 32
  21. 2024SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Datacited by 27
  22. 2023Generating Private Synthetic Data with Genetic Algorithmscited by 18
  23. 2024Differentially Private Release of Israel's National Registry of Live Birthscited by 17
  24. 2022Synthetic Graph Generation to Benchmark Graph Learningcited by 16
  25. 2025Benchmarking Differentially Private Tabular Data Synthesiscited by 14
  26. 2023Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utilitycited by 13
  27. 2023PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacycited by 11
  28. 2024Privacy Vulnerabilities in Marginals-based Synthetic Datacited by 10
  29. 2022On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulationcited by 8
  30. 2024Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Datacited by 8
  31. 2022Secure Multiparty Computation for Synthetic Data Generation from Distributed Datacited by 6
  32. 2023FLAIM: AIM-based Synthetic Data Generation in the Federated Settingcited by 6
  33. 2024Public-data Assisted Private Stochastic Optimization: Power and Limitationscited by 6
  34. 2023Privately generating tabular data using language modelscited by 5
  35. 2024CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sourcescited by 4
  36. 2025Leveraging Vertical Public-Private Split for Improved Synthetic Data Generationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 7.

9 papers.

  1. 2026Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing (Extended Version)cited by 2
  2. 2026Differentially Private Synthetic Data via APIs 4: Tabular Datacited by 2
  3. 2026Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Datacited by 1
  4. 2025Population synthesis with geographic coordinatescited by 0
  5. 2025One-Shot Secure Aggregation: A Hybrid Cryptographic Protocol for Private Federated Learning in IoTcited by 0
  6. 2026JANUS: Structured Bidirectional Generation for Guaranteed Constraints and Analytical Uncertaintycited by 0
  7. 2026Fixed-Parameter Tractability of Private Synthetic Data Generationcited by 0
  8. 2026Canaries in the Bank: Auditing User-Level Privacy in Private Evolutioncited by 0
  9. 2026PEG-Tab: Sampling-Time Record Repair and Release Control for Tabular Synthesiscited by 0