A simple and direct privacy-preserving classification scheme

Abstract

An inconsistency-based feature reduction method is firstly proposed in this paper, based on which a simple and direct rule extraction method for data classification is addressed. Because the proposed classification method depends directly on data inconsistency without leaving any value contents of datasets, it could be utilized to build a novel privacy-preserving scheme for isomorphic distributed data. In this paper, a simple privacy-preserving classification model is proposed based on the inconsistency-based feature reduction and its direct rule extraction method. Experimental results on benchmark datasets from UCI show that our method is both in good correctness performance and model efficiency. © 2011 Springer-Verlag Berlin Heidelberg.