mlsanityData to 7 Oct 2026

A Primer on Private Statistics

arXiv 2005.00010 ↗First posted 30 Apr 2020stat.ML cs.CR cs.DScited by 30

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Abstract (arXiv)

Differentially private statistical estimation has seen a flurry of developments over the last several years. Study has been divided into two schools of thought, focusing on empirical statistics versus population statistics. We suggest that these two lines of work are more similar than different by giving examples of methods that were initially framed for empirical statistics, but can be applied just as well to population statistics. We also provide a thorough coverage of recent work in this area.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 7; 2023: 7; 2024: 6; 2025: 3; 2026: 1.

30 papers.

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  1. 2021Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanismcited by 50
  2. 2022Composition of Differential Privacy & Privacy Amplification by Subsamplingcited by 42
  3. 2021Private and polynomial time algorithms for learning Gaussians and beyondcited by 39
  4. 2022The Fundamental Price of Secure Aggregation in Differentially Private Federated Learningcited by 39
  5. 2021A Private and Computationally-Efficient Estimator for Unbounded Gaussianscited by 34
  6. 2022New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemmacited by 32
  7. 2022Faster Rates of Convergence to Stationary Points in Differentially Private Optimizationcited by 32
  8. 2021Differentially Private Quantilescited by 22
  9. 2023Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributionscited by 22
  10. 2023Better and Simpler Lower Bounds for Differentially Private Statistical Estimationcited by 16
  11. 2021Privately Learning Mixtures of Axis-Aligned Gaussianscited by 13
  12. 2023A Bias-Accuracy-Privacy Trilemma for Statistical Estimationcited by 12
18 more
  1. 2022Differentially Private Sampling from Distributionscited by 8
  2. 2022Instance-Optimal Differentially Private Estimationcited by 7
  3. 2023Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Freecited by 6
  4. 2023Mean Estimation Under Heterogeneous Privacy Demandscited by 6
  5. 2022Continual Mean Estimation Under User-Level Privacycited by 5
  6. 2023A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributionscited by 5
  7. 2023Private Federated Learning with Autotuned Compressioncited by 5
  8. 2024Public-data Assisted Private Stochastic Optimization: Power and Limitationscited by 5
  9. 2024Distribution Learnability and Robustnesscited by 4
  10. 2024Not All Learnable Distribution Classes are Privately Learnablecited by 3
  11. 2024Differentially Private Optimization with Sparse Gradientscited by 2
  12. 2024Differentially Private Prototypes for Imbalanced Transfer Learningcited by 2
  13. 2021Differentially Private Ensemble Classifiers for Data Streamscited by 1
  14. 2024Privacy Preserving Adaptive Experiment Designcited by 1
  15. 2025How Private is Your Attention? Bridging Privacy with In-Context Learningcited by 0
  16. 2025Beyond ATE: Multi-Criteria Design for A/B Testingcited by 0
  17. 2025Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graphcited by 0
  18. 2026Sequential Auditing for f-Differential Privacycited by 0