Differentially Private Confidence Intervals

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.

References in corpus (1)

• PSI (Ψ): a Private data Sharing Interface

Cited by in corpus (12)

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• On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians
• Parametric Bootstrap for Differentially Private Confidence Intervals
• Differentially Private Confidence Intervals for Proportions under Stratified Random Sampling
• A Private and Computationally-Efficient Estimator for Unbounded Gaussians
• Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy
• Private and polynomial time algorithms for learning Gaussians and beyond
• Privately Learning Mixtures of Axis-Aligned Gaussians
• Privacy-Preserving Randomized Controlled Trials: A Protocol for Industry Scale Deployment