Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/108587
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Type: | Conference paper |
Title: | A privacy-preserving data publishing method for multiple numerical sensitive attributes via clustering and multi-sensitive bucketization |
Author: | Liu, Q. Shen, H. Sang, Y. |
Citation: | Proceedings - International Symposium on Parallel Architectures, Algorithms and Programming, PAAP, 2014, pp.220-223 |
Publisher: | IEEE |
Issue Date: | 2014 |
Series/Report no.: | International Symposium on Parallel Architectures Algorithms and Programming |
ISBN: | 9781479938445 |
ISSN: | 2168-3034 2168-3042 |
Conference Name: | 6th International Symposium on Parallel Architectures, Algorithms, and Programming (PAAP) (13 Jul 2014 - 15 Jul 2014 : Beijing, China) |
Statement of Responsibility: | Qinghai Liu, Hong Shen, 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 may contain multiple numerical sensitive attributes. Directly applying the existing single-numerical-sensitive-attribute and multiplecategorical- sensitive-attributes privacy preserving techniques often causes unexpected private information disclosure. They are particularly prone to the proximity breach, a privacy threat specific to numerical sensitive attributes in data publication. In this paper we propose a privacy-preserving data publishing method, namely MNSACM, that uses the ideas of clustering and Multi-Sensitive Bucketization (MSB) to publish microdata with multiple numerical sensitive attributes. Through an example we show the effectiveness of this method in privacy protection to multiple numerical sensitive attributes. |
Keywords: | Privacy-preserving; anonymity; numerical sensitive attribute; clustering; MSB; method |
Rights: | © 2014 IEEE |
DOI: | 10.1109/PAAP.2014.56 |
Published version: | http://dx.doi.org/10.1109/paap.2014.56 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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RA_hdl_108587.pdf Restricted Access | Restricted Access | 141.42 kB | Adobe PDF | View/Open |
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