DocumentCode
2929666
Title
(K, G)-anonymity model based on grey relational analysis
Author
Zhang Qishan ; Lin Zhensi ; Zheng Qunhua ; Liu Hong
Author_Institution
Sch. of Manage., Fuzhou Univ., Fuzhou, China
fYear
2013
fDate
15-17 Nov. 2013
Firstpage
16
Lastpage
19
Abstract
k-anonymity is an effective method of privacy preserving. However, some traditional k-anonymity models do not capture diversity and dispersibility of sensitive values in each equivalence class, which makes the privacy disclosure of anonymity table occur easily. In this paper, an advanced (k, g)-anonymity model for numerical data is proposed, and a (k, g)-MDAV algorithm is designed to achieve (k, g)-algorithm. Experimental results show that the algorithm can lower the risk of privacy disclosure while maintaining the data availability.
Keywords
data privacy; equivalence classes; grey systems; (k, g)-MDAV algorithm; (k, g)-anonymity model; anonymity table privacy disclosure risk; data availability; equivalence class; grey relational analysis; maximum distance to average vector algorithm; Algorithm design and analysis; Data models; Data privacy; Educational institutions; Numerical models; Privacy; Silicon; (k; Grey relational analysis; g)-anonymity; k-anonymity; microaggregation;
fLanguage
English
Publisher
ieee
Conference_Titel
Grey Systems and Intelligent Services, 2013 IEEE International Conference on
Conference_Location
Macao
ISSN
2166-9430
Print_ISBN
978-1-4673-5247-5
Type
conf
DOI
10.1109/GSIS.2013.6714730
Filename
6714730
Link To Document