mlsanityData to 7 Oct 2026

Data Synthesis based on Generative Adversarial Networks

arXiv 1806.03384 ↗First posted 9 Jun 2018cs.DB cs.CRcited by 184

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Abstract (arXiv)

Privacy is an important concern for our society where sharing data with partners or releasing data to the public is a frequent occurrence. Some of the techniques that are being used to achieve privacy are to remove identifiers, alter quasi-identifiers, and perturb values. Unfortunately, these approaches suffer from two limitations. First, it has been shown that private information can still be leaked if attackers possess some background knowledge or other information sources. Second, they do not take into account the adverse impact these methods will have on the utility of the released data. In this paper, we propose a method that meets both requirements. Our method, called table-GAN, uses generative adversarial networks (GANs) to synthesize fake tables that are statistically similar to the original table yet do not incur information leakage. We show that the machine learning models trained using our synthetic tables exhibit performance that is similar to that of models trained using the original table for unknown testing cases. We call this property model compatibility. We believe that anonymization/perturbation/synthesis methods without model compatibility are of little value. We used four real-world datasets from four different domains for our experiments and conducted in-depth comparisons with state-of-the-art anonymization, perturbation, and generation techniques. Throughout our experiments, only our method consistently shows a balance between privacy level and model compatibility.

Key facts. Cited by 184 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 18; 2022: 28; 2023: 25; 2024: 52; 2025: 40; 2026: 21.

184 papers.

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  1. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  2. 2022Language Models are Realistic Tabular Data Generatorscited by 290
  3. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  4. 2021CTAB-GAN: Effective Table Data Synthesizingcited by 156
  5. 2023REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformerscited by 93
  6. 2022STaSy: Score-based Tabular data Synthesiscited by 85
  7. 2023CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesiscited by 78
  8. 2022CTAB-GAN+: Enhancing Tabular Data Synthesiscited by 70
  9. 2022Synthetic Data -- what, why and how?cited by 66
  10. 2024TabDiff: a Mixed-type Diffusion Model for Tabular Data Generationcited by 60
  11. 2023Generative Table Pre-training Empowers Models for Tabular Predictioncited by 55
  12. 2023Membership Inference of Diffusion Modelscited by 46
38 more
  1. 2022Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Taskscited by 42
  2. 2023TabuLa: Harnessing Language Models for Tabular Data Synthesiscited by 42
  3. 2023MissDiff: Training Diffusion Models on Tabular Data with Missing Valuescited by 38
  4. 2024A Comprehensive Survey on Data Augmentationcited by 38
  5. 2021Copula Flows for Synthetic Data Generationcited by 35
  6. 2021TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networkscited by 34
  7. 2022SOS: Score-based Oversampling for Tabular Datacited by 29
  8. 2024How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Datacited by 29
  9. 2021OCT-GAN: Neural ODE-based Conditional Tabular GANscited by 27
  10. 2022TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Datacited by 27
  11. 2024A Systematic Review on Long-Tailed Learningcited by 27
  12. 2025A Comprehensive Survey of Synthetic Tabular Data Generationcited by 27
  13. 2021Towards Generating Real-World Time Series Datacited by 26
  14. 2022Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesiscited by 24
  15. 2024Generative AI for Visualization: State of the Art and Future Directionscited by 23
  16. 2022It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teachercited by 20
  17. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  18. 2023Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstructioncited by 18
  19. 2024Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analyticscited by 18
  20. 2025Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraintscited by 18
  21. 2024Systematic Assessment of Tabular Data Synthesiscited by 17
  22. 2024Differentially Private Tabular Data Synthesis using Large Language Modelscited by 16
  23. 2021Generative Adversarial Networks: A Survey Towards Private and Secure Applicationscited by 15
  24. 2022Row Conditional-TGAN for generating synthetic relational databasescited by 15
  25. 2024Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AIcited by 15
  26. 2024TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Modelscited by 15
  27. 2021Model-Based Counterfactual Synthesizer for Interpretationcited by 14
  28. 2023The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasetscited by 14
  29. 2024Automated data processing and feature engineering for deep learning and big data applications: a surveycited by 14
  30. 2024gTBLS: Generating Tables from Text by Conditional Question Answeringcited by 13
  31. 2024An improved tabular data generator with VAE-GMM integrationcited by 13
  32. 2025TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Datacited by 13
  33. 2025A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyondcited by 13
  34. 2021Causal-TGAN: Generating Tabular Data Using Causal Generative Adversarial Networkscited by 12
  35. 2024Balanced Mixed-Type Tabular Data Synthesis with Diffusion Modelscited by 12
  36. 2024Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Modelscited by 12
  37. 2025The DCR Delusion: Measuring the Privacy Risk of Synthetic Datacited by 11
  38. 2021Synthesising Multi-Modal Minority Samples for Tabular Datacited by 10

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