REVIEW 2 cited by
Differentially Private Confidence Intervals
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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 {\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.
Forward citations
Cited by 2 Pith papers
-
Private Geometric Median in Nearly-Linear Time
A new (epsilon, delta)-DP algorithm computes an alpha-multiplicative geometric median approximation in O~(nd + d/alpha^2) time, matching the optimal sample complexity of prior work.
-
PRECISE: PRivacy-loss-Efficient and Consistent Inference based on poSterior quantilEs
PRECISE is a proposed DP posterior-quantile interval method whose central privacy guarantee rests on an incorrect sensitivity bound.
Discussion (0). Continue with ORCID to comment.