Differentially Private Confidence Intervals

By Wenxin Du, Canyon Foot, Monica Moniot, Andrew Bray, Adam Groce. 2020

52 citations recorded by the source corpus.

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 {\epsilon} = 0.1 and n = 2782) our algorithm yields an interval that is only 1/15th the size of the standard set by prior work.

Subfields: Artificial Intelligence, Management Science and Operations Research

Citations in the corpus

• CoinPress: Practical Private Mean and Covariance Estimation (2020, unknown): “[14] applied coordinatewise (SYMQ); (5) Multivariate naïve method (MVMREC with t = 1) (t = 1); (6) MVMREC for various t > 1 (t = ♠, for integer ♠ > 1).”