mlsanityData to 7 Oct 2026

Differentially Private Confidence Intervals

arXiv 2001.02285 ↗First posted 7 Jan 2020stat.ME cs.CRcited by 20

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Abstract (arXiv)

Confidence intervals for the population mean of normally distributed data are some of the most standard statistical outputs one might want from a database. In this work we give practical differentially private algorithms for this task. We provide five algorithms and then compare them to each other and to prior work. We give concrete, experimental analysis of their accuracy and find that our algorithms provide much more accurate confidence intervals than prior work. For example, in one setting (with ε = 0.1 and n = 2782) our algorithm yields an interval that is only 1/15th the size of the standard set by prior work.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jan 2020.

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Citing papers per year: 2021: 8; 2022: 3; 2023: 5; 2024: 0; 2025: 4.

20 papers.

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  1. 2021Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanismcited by 50
  2. 2021Covariance-Aware Private Mean Estimation Without Private Covariance Estimationcited by 46
  3. 2021Private and polynomial time algorithms for learning Gaussians and beyondcited by 39
  4. 2021Private Robust Estimation by Stabilizing Convex Relaxationscited by 38
  5. 2021FriendlyCore: Practical Differentially Private Aggregationcited by 34
  6. 2021A Private and Computationally-Efficient Estimator for Unbounded Gaussianscited by 34
  7. 2022New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemmacited by 32
  8. 2021Privately Learning Mixtures of Axis-Aligned Gaussianscited by 13
  9. 2023A Bias-Accuracy-Privacy Trilemma for Statistical Estimationcited by 12
  10. 2022Differentially Private Bootstrap: New Privacy Analysis and Inference Strategiescited by 9
  11. 2021Non-parametric Differentially Private Confidence Intervals for the Mediancited by 7
  12. 2022On the Statistical Complexity of Estimation and Testing under Privacy Constraintscited by 6
8 more
  1. 2023PLAN: Variance-Aware Private Mean Estimationcited by 6
  2. 2023A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributionscited by 5
  3. 2023DPM: Clustering Sensitive Data through Separationcited by 1
  4. 2025Private Geometric Median in Nearly-Linear Timecited by 1
  5. 2025Differentially Private E-Valuescited by 1
  6. 2023Instance-Specific Asymmetric Sensitivity in Differential Privacycited by 0
  7. 2025Privately Estimating Black-Box Statisticscited by 0
  8. 2025Differentially Private Nonparametric Confidence Intervals Under Minimal Distributional Assumptionscited by 0