mlsanityData to 7 Oct 2026

CoinPress: Practical Private Mean and Covariance Estimation

arXiv 2006.06618 ↗First posted 11 Jun 2020stat.ML cs.CR cs.DScited by 74

Not on the map. Why?

Abstract (arXiv)

We present simple differentially private estimators for the mean and covariance of multivariate sub-Gaussian data that are accurate at small sample sizes. We demonstrate the effectiveness of our algorithms both theoretically and empirically using synthetic and real-world datasets -- showing that their asymptotic error rates match the state-of-the-art theoretical bounds, and that they concretely outperform all previous methods. Specifically, previous estimators either have weak empirical accuracy at small sample sizes, perform poorly for multivariate data, or require the user to provide strong a priori estimates for the parameters.

Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 16; 2023: 17; 2024: 9; 2025: 13; 2026: 5.

74 papers.

Sort:

  1. 2021Robust and Differentially Private Mean Estimationcited by 66
  2. 2021Differential privacy and robust statistics in high dimensionscited by 64
  3. 2021Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanismcited by 50
  4. 2021Public Data-Assisted Mirror Descent for Private Model Trainingcited by 49
  5. 2021Covariance-Aware Private Mean Estimation Without Private Covariance Estimationcited by 46
  6. 2021Private and polynomial time algorithms for learning Gaussians and beyondcited by 39
  7. 2021Private Robust Estimation by Stabilizing Convex Relaxationscited by 38
  8. 2021FriendlyCore: Practical Differentially Private Aggregationcited by 34
  9. 2021A Private and Computationally-Efficient Estimator for Unbounded Gaussianscited by 34
  10. 2022New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemmacited by 32
  11. 2022Private Estimation with Public Datacited by 31
  12. 2022DP-PCA: Statistically Optimal and Differentially Private PCAcited by 24
38 more
  1. 2023Polynomial Time and Private Learning of Unbounded Gaussian Mixture Modelscited by 19
  2. 2021Differentially-Private Clustering of Easy Instancescited by 17
  3. 2021Differential Privacy Over Riemannian Manifoldscited by 16
  4. 2022Differentially Private Covariance Revisitedcited by 15
  5. 2023Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distancecited by 14
  6. 2021Privately Learning Mixtures of Axis-Aligned Gaussianscited by 13
  7. 2022Private High-Dimensional Hypothesis Testingcited by 12
  8. 2022Differentially private Riemannian optimizationcited by 12
  9. 2023A Bias-Accuracy-Privacy Trilemma for Statistical Estimationcited by 12
  10. 2023A Fast Algorithm for Adaptive Private Mean Estimationcited by 11
  11. 2022Differentially Private Regression with Unbounded Covariatescited by 10
  12. 2022Differentially Private Bootstrap: New Privacy Analysis and Inference Strategiescited by 9
  13. 2021Non-parametric Differentially Private Confidence Intervals for the Mediancited by 7
  14. 2024Insufficient Statistics Perturbation: Stable Estimators for Private Least Squarescited by 7
  15. 2025Towards hyperparameter-free optimization with differential privacycited by 7
  16. 2022(Nearly) Optimal Private Linear Regression via Adaptive Clippingcited by 6
  17. 2022Shape And Structure Preserving Differential Privacycited by 6
  18. 2022On the Statistical Complexity of Estimation and Testing under Privacy Constraintscited by 6
  19. 2023PLAN: Variance-Aware Private Mean Estimationcited by 6
  20. 2023Mean Estimation Under Heterogeneous Privacy Demandscited by 6
  21. 2022Learning-Augmented Private Algorithms for Multiple Quantile Releasecited by 5
  22. 2022Continual Mean Estimation Under User-Level Privacycited by 5
  23. 2023A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributionscited by 5
  24. 2023Private Federated Learning with Autotuned Compressioncited by 5
  25. 2023Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samplescited by 5
  26. 2023A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacycited by 4
  27. 2024A Universal Metric of Dataset Similarity for Cross-silo Federated Learningcited by 4
  28. 2021Gaussian Processes with Differential Privacycited by 3
  29. 2023Subset-Based Instance Optimality in Private Estimationcited by 3
  30. 2023Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivitycited by 3
  31. 2023Differentially private projection-depth-based medianscited by 3
  32. 2024Not All Learnable Distribution Classes are Privately Learnablecited by 3
  33. 2025An Iterative Algorithm for Differentially Private k-PCA with Adaptive Noisecited by 3
  34. 2022Private independence testing across two partiescited by 2
  35. 2022Differentially private multivariate medianscited by 2
  36. 2023About the Cost of Central Privacy in Density Estimationcited by 2
  37. 2024Resampling methods for private statistical inferencecited by 2
  38. 2024Differentially Private Prototypes for Imbalanced Transfer Learningcited by 2

50 most cited of 74 All 74 (JSON)