Probability comprehension of differential privacy for privacy protection algorithms: A new measure

被引：4

作者：

Nie, Weilin ^{[1
]}

Wang, Cheng ^{[1
]}

机构：

[1] Huizhou Univ, Dept Math, Huizhou, Guangdong, Peoples R China

来源：

INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING | 2017年 / 15卷 / 04期

关键词：

Differential privacy; privacy protection; probability increasing bounds;

D O I：

10.1142/S0219691317500333

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Differential privacy becomes a standard for evaluating the privacy protection performance for an algorithm these years. However, the definition of differential privacy seems not so easy to understand as the classical k-anonymity and etc. In this paper, we propose a new measure which is more comprehensible. Some properties of such measure are investigated and the relationship between our new definition and differential privacy is studied.

引用

页数：6

共 7 条

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