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.

本文提出五种实用的差分隐私算法用于计算正态分布数据的总体均值的置信区间，并将其与以前的工作进行比较，实验结果表明本文算法提供比以前的工作更准确的置信区间。

差分隐私置信区间

Differentially Private Confidence Intervals

Rejoinder to ``Demystifying Double Robustness: A Comparison of Alternative
Strategies for Estimating a Population Mean from Incomplete Data''
[arXiv:0804.2958]

针对一篇关于不完整数据中估计总体均值的研究，提出了对于双重稳健性的回应。

解读反驳：解析双重鲁棒性：比较从不完整数据中估计总体均值的备选策略

Rejoinder: Demystifying Double Robustness: A Comparison of Alternative  Strategies for Estimating a Population Mean from Incomplete Data

Comment on ``Demystifying Double Robustness: A Comparison of Alternative
Strategies for Estimating a Population Mean from Incomplete Data''
[arXiv:0804.2958]

探讨使用双重稳健法和替代策略估算不完整数据中总体均值的方法。