Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/108836
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Type: Journal article
Title: Privacy-preserving data publishing for multiple numerical sensitive attributes
Author: Liu, Q.
Shen, H.
Sang, Y.
Citation: Tsinghua Science and Technology, 2015; 20(3):246-254
Publisher: Tsinghua University Press
Issue Date: 2015
ISSN: 1007-0214
1878-7606
Statement of
Responsibility: 
Qinghai Liu, Hong Shen, and Yingpeng Sang
Abstract: 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.
Rights: Copyright Status Unknown
RMID: 0030040670
DOI: 10.1109/TST.2015.7128936
Appears in Collections:Computer Science publications

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