作者
Tiancheng Li, Ninghui Li, Jian Zhang, Ian Molloy
发表日期
2010/11/29
期刊
IEEE transactions on knowledge and data engineering
卷号
24
期号
3
页码范围
561-574
出版商
IEEE
简介
Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data …
学术搜索中的文章
T Li, N Li, J Zhang, I Molloy - IEEE transactions on knowledge and data engineering, 2010