Journal article

Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning

Benjamin IP Rubinstein, Peter L Bartlett, Ling Huang, Nina Taft

Journal of Privacy and Confidentiality | Journal of Privacy and Confidentiality

Abstract

The ubiquitous need for analyzing privacy-sensitive information—including health records, personal communications, product ratings and social network data—is driving significant interest in privacy-preserving data analysis across several research communities. This paper explores the release of Support Vector Machine (SVM) classifiers while preserving the privacy of training data. The SVM is a popular machine learning method that maps data to a high-dimensional feature space before learning a linear decision boundary. We present efficient mechanisms for finite-dimensional feature mappings and for (potentially infinite-dimensional) mappings with translation-invariant kernels. In the latter cas..

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