The Community for Technology Leaders
2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) (2014)
Beijing, China
July 13, 2014 to July 15, 2014
ISSN: 2168-3034
ISBN: 978-1-4799-3844-5
pp: 220-223
Anonymized data publication has received considerableattention from the research community in recent years. Fornumerical sensitive attributes, most of the existing privacy preservingdata publishing techniques concentrate on microdata withmultiple categorical sensitive attributes or only one numericalsensitive attribute. However, many real-world applications maycontain multiple numerical sensitive attributes. Directly applyingthe existing single-numerical-sensitive-attribute and multiplecategorical-sensitive-attributes privacy preserving techniques oftencauses unexpected private information disclosure. They areparticularly prone to the proximity breach, a privacy threatspecific to numerical sensitive attributes in data publication. In this paper we propose a privacy-preserving data publishingmethod, namely MNSACM, that uses the ideas of clustering andMulti-Sensitive Bucketization (MSB) to publish microdata withmultiple numerical sensitive attributes. Through an example weshow the effectiveness of this method in privacy protection tomultiple numerical sensitive attributes.
Remuneration, Publishing, Data privacy, Privacy, Educational institutions, Numerical models, Computers,method, privacy-preserving, anonymity, numerical sensitive attribute, clustering, MSB
Qinghai Liu, Hong Shen, Yingpeng Sang, "A Privacy-Preserving Data Publishing Method for Multiple Numerical Sensitive Attributes via Clustering and Multi-sensitive Bucketization", 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), vol. 00, no. , pp. 220-223, 2014, doi:10.1109/PAAP.2014.56
89 ms
(Ver 3.3 (11022016))