Privacy-preserving data publishing for multiple numerical sensitive attributes

Qinghai Liu, Hong Shen, Yingpeng Sang

研究成果: Article同行評審

37 引文 斯高帕斯(Scopus)

摘要

Anonymized data publication has received considerable attention from the research community in recent years. For numerical sensitive attributes, most of the existing privacy-preserving data publishing techniques concentrate on microdata with multiple categorical sensitive attributes or only one numerical sensitive attribute. However, many real-world applications can contain multiple numerical sensitive attributes. Directly applying the existing privacy-preserving techniques for single-numerical-sensitive-attribute and multiple-categorical-sensitive-attributes often causes unexpected disclosure of private information. These techniques are particularly prone to the proximity breach, which is a privacy threat specific to numerical sensitive attributes in data publication. In this paper, we propose a privacy-preserving data publishing method, namely MNSACM, which uses the ideas of clustering and Multi-Sensitive Bucketization (MSB) to publish microdata with multiple numerical sensitive attributes. We use an example to show the effectiveness of this method in privacy protection when using multiple numerical sensitive attributes.

原文English
文章編號7128936
頁(從 - 到)246-254
頁數9
期刊Tsinghua Science and Technology
20
發行號3
DOIs
出版狀態Published - 1 6月 2015
對外發佈

指紋

深入研究「Privacy-preserving data publishing for multiple numerical sensitive attributes」主題。共同形成了獨特的指紋。

引用此