论文标题
差异私人置信区间
Differentially Private Confidence Intervals
论文作者
论文摘要
人口平均值的置信区间是正态分布数据的平均值是一种从数据库中可能想要的最标准的统计输出。在这项工作中，我们为此任务提供了实用的私人算法。我们提供五种算法，然后将它们彼此进行比较和先前的工作。我们对其准确性进行具体的实验分析，发现我们的算法比先前的工作提供了更准确的置信区间。例如，在一种设置（使用ε= 0.1和n = 2782）中，我们的算法产生的间隔仅是先前工作设置的标准大小的1/15。
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.
