Paper Info
Title
On the Design and Analysis of the Privacy-Preserving SVM Classifier
Abstract
The support vector machine (SVM) is a widely used tool in classification problems. The SVM trains a classifier by solving an optimization problem to decide which instances of the training data set are support vectors, which are the necessarily informative instances to form the SVM classifier. Since support vectors are intact tuples taken from the training data set, releasing the SVM classifier for public use or shipping the SVM classifier to clients will disclose the private content of support vectors. This violates the privacy-preserving requirements for some legal or commercial reasons. The problem is that the classifier learned by the SVM inherently violates the privacy. This privacy violation problem will restrict the applicability of the SVM. To the best of our knowledge, there has not been work extending the notion of privacy preservation to tackle this inherent privacy violation problem of the SVM classifier. In this paper, we exploit this privacy violation problem, and propose an approach to postprocess the SVM classifier to transform it to a privacy-preserving classifier which does not disclose the private content of support vectors. The postprocessed SVM classifier without exposing the private content of training data is called Privacy-Preserving SVM Classifier (abbreviated as PPSVC). The PPSVC is designed for the commonly used Gaussian kernel function. It precisely approximates the decision function of the Gaussian kernel SVM classifier without exposing the sensitive attribute values possessed by support vectors. By applying the PPSVC, the SVM classifier is able to be publicly released while preserving privacy. We prove that the PPSVC is robust against adversarial attacks. The experiments on real data sets show that the classification accuracy of the PPSVC is comparable to the original SVM classifier.
Year
DOI
Venue
2011
10.1109/TKDE.2010.193
IEEE Trans. Knowl. Data Eng.
Keywords
Field
DocType
privacy-preserving data mining,optimisation,inherent privacy violation problem,postprocessed svm classifier,data privacy,gaussian kernel svm classifier,pattern classification,privacy-preserving classifier,privacy-preserving requirements,gaussian kernel function,privacy-preserving svm classifier,svm classifier,privacy violation problem,support vector,original svm classifier,support vector machine,optimization,intact tuples,gaussian processes,private content,support vector machines.,classification,support vector machines,training data,privacy,kernel
Structured support vector machine,Data mining,Margin (machine learning),Ranking SVM,Computer science,Artificial intelligence,Information privacy,Classifier (linguistics),Pattern recognition,Support vector machine,Margin classifier,Machine learning,Quadratic classifier
Journal
Volume
Issue
ISSN
23
11
1041-4347
Citations 
PageRank 
References 
45
1.47
17
Authors
2
Name
Order
Citations
PageRank
Keng-Pei Lin111711.61
Ming Chen265071277.71