通用私人估算器

我们为纯差分值隐私下的统计平均值、差值和规模（特别是四分位数范围）提供通用估计值。这些估算器是通用的，因为它们在研发上处理任意的、未知的分布 P，同时产生强大的功利保证，但行为不端的 P 除外。对于某些分布家族（如高斯人或重尾分布），我们表明，我们的通用估算器与现有估算器相匹配或改进，这些估计器通常专为给定家庭设计，并且根据 P 的平均值和方差的优先级假设。删除这些边界假设是令人惊讶的，因为现有工作认为，这些假设在纯粹的差别隐私下是必要的。

原文题目：Universal Private Estimators

原文：We present universal estimators for the statistical mean, variance, and scale (in particular, the interquartile range) under pure differential privacy. These estimators are universal in the sense that they work on an arbitrary, unknown distribution P over R, while yielding strong utility guarantees except for ill-behaved P. For certain distribution families like Gaussians or heavy-tailed distributions, we show that our universal estimators match or improve existing estimators, which are often specifically designed for the given family and under priori boundedness assumptions on the mean and variance of P. The removal of these boundedness assumptions is surprising, as existing work believes that they are necessary under pure differential privacy.