Title: Granular computing in privacy-preserving data mining
Author: Justin Zhan
Address: Heinz College & Cylab Japan, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, USA
Abstract: Granular computing is an emerging computing paradigm of information processing. It concerns the processing of complex information entities, called 'information granules', which appear in the process of data abstraction and derivation of knowledge from information. The granular computing paradigm has been applied to many applications and we will address the applications of granular computing in the area of privacy-preserving data mining. We will use privacy-preserving association rule mining, privacy-preserving k-nearest neighbour classification and privacy-preserving support vector machine classification to illustrate how the paradigm of granular computing has been applied.
Keywords: granular computing; privacy preservation; privacy protection; classification; data mining; association rule mining; k-nearest neighbour; support vector machines; SVM.
Int. J. of Granular Computing, Rough Sets and Intelligent Systems, 2010 Vol.1, No.3, pp.272 - 288
Available online: 30 Nov 2009