Clustering-based algorithm for data sensitive attributes anonymous protection
基于聚类的数据敏感属性匿名保护算法*

In order to prevent the disclosure of data sensitive attributes, it requires preserving the anonymity of data sensitive attributes. The current algorithm that has proposed to meet l-diversity is mostly based on the hierarchy, which can lead to unnecessary information loss. For this reason, this paper proposed a clustering-based algorithm for data sensitive attributes anonymous protection, it adopted an improved distance measure method which was from achieving k-anonymity by clustering in attribute hierarchical structures and combined clustering together, the algorithm in accordance with the requirements of l-diversity model clustering of data sets. Experimental results show that the algorithm can not only protect anonymity of sensitive attri-butes in data set, but also reduce the extent of information losses.