mlsanityData to 7 Oct 2026

DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation

arXiv 2307.10430 ↗First posted 19 Jul 2023cs.LG cs.CRcited by 13

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Abstract (arXiv)

The generation of synthetic tabular data that preserves differential privacy is a problem of growing importance. While traditional marginal-based methods have achieved impressive results, recent work has shown that deep learning-based approaches tend to lag behind. In this work, we present Differentially-Private TaBular AutoRegressive Transformer (DP-TBART), a transformer-based autoregressive model that maintains differential privacy and achieves performance competitive with marginal-based methods on a wide variety of datasets, capable of even outperforming state-of-the-art methods in certain settings. We also provide a theoretical framework for understanding the limitations of marginal-based approaches and where deep learning-based approaches stand to contribute most. These results suggest that deep learning-based techniques should be considered as a viable alternative to marginal-based methods in the generation of differentially private synthetic tabular data.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 23 of its 38 references that are in the map. First posted on arXiv 19 Jul 2023.

Builds on

23 of 38 references.

References · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  4. 2016Pointer Sentinel Mixture Modelscited by 2,992
  5. 2016Deep Learning with Differential Privacycited by 2,682
  6. 2019Modeling Tabular data using Conditional GANcited by 695
  7. 2021Large Language Models Can Be Strong Differentially Private Learnerscited by 405
  8. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 373
  9. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 347
  10. 2022Language Models are Realistic Tabular Data Generatorscited by 290
  11. 2020Differentially Private Learning Needs Better Features (or Much More Data)cited by 233
  12. 2022Unlocking High-Accuracy Differentially Private Image Classification through Scalecited by 218
11 more
  1. 2021CTAB-GAN: Effective Table Data Synthesizingcited by 156
  2. 2022AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Datacited by 88
  3. 2022Toward Training at ImageNet Scale with Differential Privacycited by 87
  4. 2021Benchmarking Differentially Private Synthetic Data Generation Algorithmscited by 80
  5. 2020DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generationcited by 78
  6. 2019Graphical-model based estimation and inference for differential privacycited by 73
  7. 2022CTAB-GAN+: Enhancing Tabular Data Synthesiscited by 70
  8. 2021Iterative Methods for Private Synthetic Data: Unifying Framework and New Methodscited by 58
  9. 2020Differentially Private Synthetic Data: Applied Evaluations and Enhancementscited by 36
  10. 2023Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generationcited by 15
  11. 2022Generative Modeling of Complex Datacited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 4; 2026: 4.

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  1. 2024Differentially Private Tabular Data Synthesis using Large Language Modelscited by 16
  2. 2024HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protectioncited by 16
  3. 2025TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Datacited by 13
  4. 2024Unmasking Trees for Tabular Datacited by 4
  5. 2024Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Datacited by 4
  6. 2025Graph-Conditional Flow Matching for Relational Data Generationcited by 3
  7. 2026Differentially Private Synthetic Data via APIs 4: Tabular Datacited by 2
  8. 2024Qrlew: Rewriting SQL into Differentially Private SQLcited by 1
  9. 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspectivecited by 1
  10. 2026Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Datacited by 1
  11. 2026In Transformer We Trust? A Perspective on Transformer Architecture Failure Modescited by 1
  12. 2025MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selectioncited by 0
1 more
  1. 2025Beyond One-Size-Fits-All: Neural Networks for Differentially Private Tabular Data Synthesiscited by 0