A General Framework for Accurate and Private Mean Estimation
A General Framework for Accurate and Private Mean Estimation
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DOI：
10.1109/lsp.2022.3219356
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发表时间：
2022
影响因子：
3.9
通讯作者：
Zhouhao Yang;Xingyu Xu;Yuantao Gu
中科院分区：
文献类型：
--
作者：
Zhouhao Yang;Xingyu Xu;Yuantao Gu
在这封信中，我们提出了一个差分隐私算法，准确地估计一个潜在的人口与给定的累积分布函数的平均值。该算法在两个方面优于以往的算法。首先，我们的算法能够处理更一般类型的概率分布，可能具有非常重的尾部。其次，对于轻尾分布，我们的算法实现了更好的精度水平与更少的样本。
In this letter, we present a differentially private algorithm which accurately estimates the mean of an underlying population with given cumulative distribution function. Our algorithm outperforms the former algorithms in two aspects. First, our algorithm is capably of handling more general types of probability distributions, possibly with a very heavy tail. Second, for light-tailed distributions, our algorithm achieves a better level of accuracy with fewer samples.