面向近邻泄露的数值型敏感属性隐私保护方法

Privacy preserving approach based on proximity privacy for numerical sensitive attributes

提出一种面向近邻泄露的数值型敏感属性隐私保护方法,该方法首先在保护准标识符属性和数值型敏感属性内在关系的前提下,将数值型敏感属性进行离散化划分;然后,提出一种面向近邻泄露的隐私保护原则——(k,ε)-proximity;最后,设计了最大邻域优先算法MNF(maximal neighborhood first)来实现该原则。实验结果表明,提出的方法能在有效保护数值型敏感信息不泄露的同时保持较高的数据效用,并且保护了数据间的关系。

A model based on proximity breach for numerical sensitive attributes is proposed. At first, it divides numerical sensitive value into several intervals on the premise of protecting the internal relations between quasi-identifier attributes and numerical sensitive attributes. Secondly, it proposes a （k, ε）- proximity privacy preserving principle to defense prox- imity privacy. In the end, a maximal neighborhood first algorithm （MNF） is designed to realize the （k, ε）-proximity. The experiment results show that the proposed model can preserve privacy of sensitive data well meanwhile it can also keep a high data utility and protect the internal relations.