论文标题
最佳伽马密度与增加噪声混淆定量数据
Optimal Gamma density to Obfuscate Quantitative data with Added Noise
论文作者
论文摘要
在数据集中保护个人的隐私远比从中进行统计推断同样重要。如果手中的数据是定量的，那么保护它的通常方法是向单个数据值添加噪声。但是，应该使用理想的密度来产生噪声，以便我们可以最大程度地使用数据，而不会损害隐私？在本文中，我们解决了这个问题，并提出了一种选择伽马家族中密度的方法，该密度是最佳的。
Protecting the privacy of individuals in a data-set is no less important than making statistical inferences from it. In case the data in hand is quantitative, the usual way to protect it is to add a noise to the individual data values. But, what should be an ideal density used to generate the noise, so that we can get the maximum use of the data, without compromising privacy? In this paper, we deal with this problem and propose a method of selecting a density within the Gamma family that is optimal for this purpose.
