Differentially Private Confidence Intervals

Understand

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.