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Privacy-Preserving SVM Classification using Non-metric MDS

Authors:
Khaled Alotaibi
Beatriz de la Iglesia

Keywords: Privacy; Classification; Data Perturbation; SVM

Abstract:
Privacy concerns are a critical issue in outsourcing data mining projects. Data owners are often unwilling to release their private data for analysis, as this may lead to data disclosure. One possible solution to address such concerns is to perturb the original data values so that they become hidden, thereby preserving privacy. This paper proposes a privacy-preserving technique using Non-metric Multidimensional Scaling, which not only preserves privacy but also maintains data utility for Support Vector Machine (SVM) classification. The perturbed data are subject to high uncertainty and have no information that can be exploited to disclose the original data. They also exhibit better class separation and compactness, which greatly eases the SVM task. The results show that the accuracy of the original and the perturbed data is similar, as the distances between the data objects both before and after the perturbation are well-preserved.

Pages: 30 to 35

Copyright: Copyright (c) IARIA, 2013

Publication date: August 25, 2013

Published in: conference

ISSN: 2162-2116

ISBN: 978-1-61208-298-1

Location: Barcelona, Spain

Dates: from August 25, 2013 to August 31, 2013