mlsanityData to 14 Oct 2026

Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data

arXiv 2304.03722 ↗First posted 7 Apr 2023cs.LGcited by 29

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Abstract (arXiv)

Generating synthetic data through generative models is gaining interest in the ML community and beyond. In the past, synthetic data was often regarded as a means to private data release, but a surge of recent papers explore how its potential reaches much further than this -- from creating more fair data to data augmentation, and from simulation to text generated by ChatGPT. In this perspective we explore whether, and how, synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs. Just as importantly, we discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data -- the most important of which is quantifying how much we can trust any finding or prediction drawn from synthetic data.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). Builds on 51 of its 92 references that are in the map. First posted on arXiv 7 Apr 2023.

Builds on

51 of 92 references.

References · 51

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  4. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  5. 2022Generative Adversarial Networkscited by 5,627
  6. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  7. 2013Auto-Encoding Variational Bayescited by 4,053
  8. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  9. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
  10. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  11. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  12. 2019Invariant Risk Minimizationcited by 1,854
38 more
  1. 2019Do ImageNet Classifiers Generalize to ImageNet?cited by 1,515
  2. 2018Datasheets for Datasetscited by 1,386
  3. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,375
  4. 2020Training Generative Adversarial Networks with Limited Datacited by 1,186
  5. 2019StarGAN v2: Diverse Image Synthesis for Multiple Domainscited by 1,098
  6. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 944
  7. 2014Certifying and removing disparate impactcited by 715
  8. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  9. 2017Unsupervised Image-to-Image Translation Networkscited by 603
  10. 2021Tabular Data: Deep Learning is Not All You Needcited by 535
  11. 2021Deep Neural Networks and Tabular Data: A Surveycited by 491
  12. 2020Reliable Fidelity and Diversity Metrics for Generative Modelscited by 434
  13. 2018Assessing Generative Models via Precision and Recallcited by 406
  14. 2017Learning to Discover Cross-Domain Relations with Generative Adversarial Networkscited by 278
  15. 2017Data Augmentation Generative Adversarial Networkscited by 278
  16. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 252
  17. 2019Few-Shot Unsupervised Image-to-Image Translationcited by 222
  18. 2016Coupled Generative Adversarial Networkscited by 216
  19. 2018Gender Bias in Neural Natural Language Processingcited by 211
  20. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  21. 2021How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Modelscited by 196
  22. 2017Generalization and Equilibrium in Generative Adversarial Nets (GANs)cited by 166
  23. 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
  24. 2019Learning from Synthetic Data for Crowd Counting in the Wildcited by 148
  25. 2020A Survey of Privacy Attacks in Machine Learningcited by 146
  26. 2019Reconstruction and Membership Inference Attacks against Generative Modelscited by 122
  27. 2020DP-CGAN: Differentially Private Synthetic Data and Label Generationcited by 118
  28. 2021DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networkscited by 109
  29. 2020Synthetic Data -- Anonymisation Groundhog Daycited by 108
  30. 2022ChatGPT: The End of Online Exam Integrity?cited by 88
  31. 2023Synthcity: facilitating innovative use cases of synthetic data in different data modalitiescited by 76
  32. 2023Membership Inference Attacks against Synthetic Data through Overfitting Detectioncited by 66
  33. 2021Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Modelscited by 57
  34. 2017ComboGAN: Unrestrained Scalability for Image Domain Translationcited by 37
  35. 2018Performing Co-Membership Attacks Against Deep Generative Modelscited by 26
  36. 2021Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Datacited by 18
  37. 2022Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populationscited by 14
  38. 2022Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networkscited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 9; 2025: 11; 2026: 3.

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  1. 2024Why Tabular Foundation Models Should Be a Research Prioritycited by 106
  2. 2023On the Constrained Time-Series Generation Problemcited by 54
  3. 2023Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Datacited by 32
  4. 2023Synthetic data, real errors: how (not) to publish and use synthetic datacited by 29
  5. 2024ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Modelscited by 28
  6. 2024Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcarecited by 17
  7. 2025TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Datacited by 14
  8. 2024A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Datacited by 10
  9. 2025Joint Relational Database Generation via Graph-Conditional Diffusion Modelscited by 9
  10. 2024Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Frameworkcited by 8
  11. 2024LaTable: Towards Large Tabular Modelscited by 8
  12. 2023FLAIM: AIM-based Synthetic Data Generation in the Federated Settingcited by 6
17 more
  1. 2024Fake it to make it: Using synthetic data to remedy the data shortage in joint multimodal speech-and-gesture synthesiscited by 5
  2. 2023Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directionscited by 4
  3. 2025RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Modelscited by 4
  4. 2024The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)cited by 3
  5. 2025Contrastive Learning-Based privacy metrics in Tabular Synthetic Datasetscited by 2
  6. 2025Leveraging Vertical Public-Private Split for Improved Synthetic Data Generationcited by 2
  7. 2026MIDST Challenge at SaTML 2025: Membership Inference over Diffusion-models-based Synthetic Tabular datacited by 2
  8. 2023On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprisecited by 1
  9. 2025Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generationcited by 1
  10. 2025STEB: In Search of the Best Evaluation Approach for Synthetic Time Seriescited by 1
  11. 2025Efficacy of Image Similarity as a Metric for Augmenting Small Dataset Retinal Image Segmentationcited by 1
  12. 2025Democratizing Tabular Data Access with an Openunicodex2013Source Syntheticunicodex2013Data SDKcited by 1
  13. 2024A primer on synthetic health datacited by 0
  14. 2025Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGNcited by 0
  15. 2025Using Synthetic Data to estimate the True Error is theoretically and practically doablecited by 0
  16. 2026Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentationcited by 0
  17. 2026CDMD: A Cross-Dataset Mixed-Type Diffusion Model for Tabular Datacited by 0