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
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;
Conference_Titel :
Grey Systems and Intelligent Services, 2013 IEEE International Conference on
Conference_Location :
Macao
Print_ISBN :
978-1-4673-5247-5
DOI :
10.1109/GSIS.2013.6714730