Title :
Privacy preserving method based on GM(1,1) and its application to data clustering
Author :
Guo, Kun ; Zhang, Qishan
Author_Institution :
Fac. of Coll. of Math. & Comput. Sci., Fuzhou Univ., Fuzhou, China
Abstract :
Protecting the users´ privacy while mining information from massive data has become a popular research topic in recent years. Perturbation and reconstruction are two common technologies in implementing privacy preserving data mining. In this paper, a novel perturbation method based on GM(1,1) model is proposed and applied to data clustering. The effectiveness and efficiency of the proposed method is demonstrated by the experiments on real-world datasets.
Keywords :
data mining; data privacy; pattern clustering; data clustering; data mining; privacy preserving method; Analytical models; Education;
Conference_Titel :
Grey Systems and Intelligent Services (GSIS), 2011 IEEE International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-61284-490-9
DOI :
10.1109/GSIS.2011.6044117
