Title: Privacy-preserving support vector machine classification

Authors: Justin Zhan, Stan Matwin

Addresses: Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. ' University of Ottawa, 800 King Edward Avenue, Ottawa, ON K1N 6N5, Canada

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 support vector machine classification which is one of data mining tasks. The goal is to obtain accurate classification results without disclosing private data.

Keywords: data privacy; security; data mining; support vector machines; SVM; classification; privacy preservation.

DOI: 10.1504/IJIIDS.2007.016686

International Journal of Intelligent Information and Database Systems, 2007 Vol.1 No.3/4, pp.356 - 385

Published online: 14 Jan 2008 *

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