Title :
Privacy preserving K-Medoids clustering
Abstract :
Privacy is an important issue in the collaborative data mining since privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. How multiple parties collaboratively conduct data mining without breaching data privacy presents a challenge. This paper seeks to investigate solutions for privacy- preserving K-Medoids clustering which is one of data mining tasks.
Keywords :
data mining; data privacy; groupware; pattern clustering; collaborative data mining; data privacy; k-medoids clustering; Chemistry; Classification algorithms; Clustering algorithms; Collaboration; Data mining; Data privacy; Immune system; Insurance; Remote sensing; Urban planning;
Conference_Titel :
Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
978-1-4244-0990-7
Electronic_ISBN :
978-1-4244-0991-4
DOI :
10.1109/ICSMC.2007.4414177
