Differentially Private Confidence Intervals

2020/01/07 by Wenxin Du, Canyon Foot, Du, Wenxin +7 · 4 citations
Computer Science · Decision Sciences · #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Methodology (stat.ME) #Privacy-Preserving Technologies in Data #Probability and Risk Models

paper · pdf · doi:10.48550/arxiv.2001.02285

openalex publication_date 2020/01/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Bootstrap Methods: Another Look at the Jackknife

Cited by

• Black-Box Differentially Private Nonparametric Confidence Intervals Under Minimal Assumptions
• Differentially Private E-Values
• Privately Estimating Black-Box Statistics
• Privacy-Preserving Randomized Controlled Trials: A Protocol for Industry Scale Deployment

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• Finite Sample Differentially Private Confidence Intervals
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• Black-Box Differentially Private Nonparametric Confidence Intervals Under Minimal Assumptions
• Interval Estimation for Binomial Proportions Under Differential Privacy
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