mlsanityData to 7 Oct 2026

dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generation

arXiv 2207.05810 ↗First posted 12 Jul 2022cs.LG cs.CRcited by 12

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Abstract (arXiv)

We propose a general, flexible, and scalable framework dpart, an open source Python library for differentially private synthetic data generation. Central to the approach is autoregressive modelling -- breaking the joint data distribution to a sequence of lower-dimensional conditional distributions, captured by various methods such as machine learning models (logistic/linear regression, decision trees, etc.), simple histogram counts, or custom techniques. The library has been created with a view to serve as a quick and accessible baseline as well as to accommodate a wide audience of users, from those making their first steps in synthetic data generation, to more experienced ones with domain expertise who can configure different aspects of the modelling and contribute new methods/mechanisms. Specific instances of dpart include Independent, an optimized version of PrivBayes, and a newly proposed model, dp-synthpop. Code: https://github.com/hazy/dpart

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 12 Jul 2022.

Builds on

8 of 18 references.

References · 8

  1. 2018Differentially Private Generative Adversarial Networkcited by 235
  2. 2021Winning the NIST Contest: A scalable and general approach to differentially private synthetic datacited by 101
  3. 2021Benchmarking Differentially Private Synthetic Data Generation Algorithmscited by 80
  4. 2021Iterative Methods for Private Synthetic Data: Unifying Framework and New Methodscited by 58
  5. 2019Diffprivlib: The IBM Differential Privacy Librarycited by 51
  6. 2021Differentially Private Query Release Through Adaptive Projectioncited by 50
  7. 2020New Oracle-Efficient Algorithms for Private Synthetic Data Releasecited by 43
  8. 2017Differentially Private Mixture of Generative Neural Networkscited by 39

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 4; 2025: 3; 2026: 1.

12 papers.

  1. 2023The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasetscited by 14
  2. 2024The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debuggingcited by 12
  3. 2024SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Modelscited by 10
  4. 2022On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulationcited by 8
  5. 2022Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Controlcited by 4
  6. 2023Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directionscited by 4
  7. 2025The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Datacited by 4
  8. 2024Synthetic Data Outliers: Navigating Identity Disclosurecited by 3
  9. 2025dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generationcited by 2
  10. 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspectivecited by 1
  11. 2024Automated Privacy-Preserving Techniques via Meta-Learningcited by 0
  12. 2025Understanding the Impact of Data Domain Extraction on Synthetic Data Privacycited by 0