DocumentCode :
263683
Title :
A Privacy-Preserving Data Publishing Method for Multiple Numerical Sensitive Attributes via Clustering and Multi-sensitive Bucketization
Author :
Qinghai Liu ; Hong Shen ; Yingpeng Sang
Author_Institution :
Sch. of Comput. & Inf. Technol., Beijing Jiaotong Univ., Beijing, China
fYear :
2014
fDate :
13-15 July 2014
Firstpage :
220
Lastpage :
223
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 multiple categorical-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 tomultiple numerical sensitive attributes.
Keywords :
data privacy; pattern clustering; MNSACM; anonymized data publication; categorical sensitive attributes; categorical-sensitive-attributes privacy preserving techniques; clustering; microdata publishing; multisensitive bucketization; numerical sensitive attributes; privacy-preserving data publishing method; private information disclosure; proximity breach; single-numerical-sensitive-attribute; Computers; Data privacy; Educational institutions; Numerical models; Privacy; Publishing; Remuneration; MSB; anonymity; clustering; method; numerical sensitive attribute; privacy-preserving;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Parallel Architectures, Algorithms and Programming (PAAP), 2014 Sixth International Symposium on
Conference_Location :
Beijing
ISSN :
2168-3034
Print_ISBN :
978-1-4799-3844-5
Type :
conf
DOI :
10.1109/PAAP.2014.56
Filename :
6916468
Link To Document :
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