mlsanityData to 7 Oct 2026

Privacy-preserving data release leveraging optimal transport and particle gradient descent

arXiv 2401.17823 ↗First posted 31 Jan 2024cs.LG cs.CRcited by 8

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Abstract (arXiv)

We present a novel approach for differentially private data synthesis of protected tabular datasets, a relevant task in highly sensitive domains such as healthcare and government. Current state-of-the-art methods predominantly use marginal-based approaches, where a dataset is generated from private estimates of the marginals. In this paper, we introduce PrivPGD, a new generation method for marginal-based private data synthesis, leveraging tools from optimal transport and particle gradient descent. Our algorithm outperforms existing methods on a large range of datasets while being highly scalable and offering the flexibility to incorporate additional domain-specific constraints.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 29 of its 58 references that are in the map. First posted on arXiv 31 Jan 2024.

Builds on

29 of 58 references.

References · 29

  1. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  2. 2018On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transportcited by 422
  3. 2018Differentially Private Generative Adversarial Networkcited by 235
  4. 2019Max-Sliced Wasserstein Distance and its use for GANscited by 118
  5. 2020DP-CGAN: Differentially Private Synthetic Data and Label Generationcited by 118
  6. 2018Generative Modeling using the Sliced Wasserstein Distancecited by 108
  7. 2021Winning the NIST Contest: A scalable and general approach to differentially private synthetic datacited by 101
  8. 2022AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Datacited by 88
  9. 2021Benchmarking Differentially Private Synthetic Data Generation Algorithmscited by 80
  10. 2019Graphical-model based estimation and inference for differential privacycited by 73
  11. 2019Sliced Wasserstein Generative Modelscited by 61
  12. 2017Sliced Wasserstein Distance for Learning Gaussian Mixture Modelscited by 59
17 more
  1. 2021Iterative Methods for Private Synthetic Data: Unifying Framework and New Methodscited by 58
  2. 2021Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergencecited by 54
  3. 2019Sparse Optimization on Measures with Over-parameterized Gradient Descentcited by 50
  4. 2021Differentially Private Query Release Through Adaptive Projectioncited by 50
  5. 2020New Oracle-Efficient Algorithms for Private Synthetic Data Releasecited by 43
  6. 2021Leveraging Public Data for Practical Private Query Releasecited by 39
  7. 2023SoK: Privacy-Preserving Data Synthesiscited by 37
  8. 2022Private Synthetic Data for Multitask Learning and Marginal Queriescited by 35
  9. 2014Dual Query: Practical Private Query Release for High Dimensional Datacited by 26
  10. 2016PrivTree: A Differentially Private Algorithm for Hierarchical Decompositionscited by 22
  11. 2017Towards Practical Differential Privacy for SQL Queriescited by 19
  12. 2021Differentially Private Sliced Wasserstein Distancecited by 19
  13. 2023Generating Private Synthetic Data with Genetic Algorithmscited by 18
  14. 2019Differentially Private Release of High-Dimensional Datasets using the Gaussian Copulacited by 8
  15. 2021DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challengescited by 7
  16. 2023Certified private data release for sparse Lipschitz functionscited by 7
  17. 2023Differentially Private Gradient Flow based on the Sliced Wasserstein Distancecited by 4

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Cited by year (2026 to 7 Oct)

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

8 papers.

  1. 2024FairJob: A Real-World Dataset for Fairness in Online Systemscited by 6
  2. 2024Online Differentially Private Synthetic Data Generationcited by 4
  3. 2024Differentially Private Synthetic Data Generation for Relational Databasescited by 2
  4. 2024Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Trainingcited by 2
  5. 2026Differentially Private Synthetic Data via APIs 4: Tabular Datacited by 2
  6. 2025Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamicscited by 0
  7. 2026Noise-Calibrated Inference from Differentially Private Sufficient Statistics in Exponential Familiescited by 0
  8. 2026Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibrationcited by 0