mlsanityData to 7 Oct 2026

Optimal Differentially Private Sampling of Unbounded Gaussians

arXiv 2503.01766 ↗First posted 3 Mar 2025cs.DS cs.CR cs.ITcited by 1

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Abstract (arXiv)

We provide the first widetildeOleft(dright)-sample algorithm for sampling from unbounded Gaussian distributions under the constraint of left(varepsilon, δright)-differential privacy. This is a quadratic improvement over previous results for the same problem, settling an open question of Ghazi, Hu, Kumar, and Manurangsi.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 31 of its 48 references that are in the map. First posted on arXiv 3 Mar 2025.

Builds on

31 of 48 references.

References · 31

  1. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 347
  2. 2018Privately Learning High-Dimensional Distributionscited by 103
  3. 2020CoinPress: Practical Private Mean and Covariance Estimationcited by 74
  4. 2024Differentially Private Synthetic Data via Foundation Model APIs 2: Textcited by 74
  5. 2021Robust and Differentially Private Mean Estimationcited by 66
  6. 2021Differential privacy and robust statistics in high dimensionscited by 64
  7. 2023Differentially Private Synthetic Data via Foundation Model APIs 1: Imagescited by 63
  8. 2019Private Hypothesis Selectioncited by 60
  9. 2021Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergencecited by 54
  10. 2019Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimationcited by 51
  11. 2021Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanismcited by 50
  12. 2018Privacy-preserving Predictioncited by 48
19 more
  1. 2022Robustness Implies Privacy in Statistical Estimationcited by 47
  2. 2021Covariance-Aware Private Mean Estimation Without Private Covariance Estimationcited by 46
  3. 2020On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussianscited by 41
  4. 2021Private and polynomial time algorithms for learning Gaussians and beyondcited by 39
  5. 2021Private Robust Estimation by Stabilizing Convex Relaxationscited by 38
  6. 2021Instance-optimal Mean Estimation Under Differential Privacycited by 37
  7. 2021A Private and Computationally-Efficient Estimator for Unbounded Gaussianscited by 34
  8. 2022Private Estimation with Public Datacited by 31
  9. 2022Pre-trained Perceptual Features Improve Differentially Private Image Generationcited by 28
  10. 2023From Robustness to Privacy and Backcited by 27
  11. 2023Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributionscited by 22
  12. 2023Private GANs, Revisitedcited by 17
  13. 2023Better and Simpler Lower Bounds for Differentially Private Statistical Estimationcited by 16
  14. 2023Private Distribution Learning with Public Data: The View from Sample Compressioncited by 14
  15. 2024Lower Bounds for Private Estimation of Gaussian Covariance Matrices under All Reasonable Parameter Regimescited by 9
  16. 2022Differentially Private Sampling from Distributionscited by 8
  17. 2023PLAN: Variance-Aware Private Mean Estimationcited by 6
  18. 2023On Differentially Private Sampling from Gaussian and Product Distributionscited by 6
  19. 2024Private Mean Estimation with Person-Level Differential Privacycited by 6

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Locally Optimal Private Sampling: Beyond the Global Minimaxcited by 2