Paper 2014/331 Original: eprint.iacr.org/2014/331 ↗

Machine Learning Classification over Encrypted Data

Abstract

Machine learning classification is used in numerous settings nowadays, such as medical or genomics predictions, spam detection, face recognition, and financial predictions. Due to privacy concerns in some of these applications, it is important that the data and the classifier remain confidential. In this work, we construct three major classification protocols that satisfy this privacy constraint: hyperplane decision, Naïve Bayes, and decision trees. These protocols may also be combined with AdaBoost. They rely on a library of building blocks for constructing classifiers securely, and we demonstrate the versatility of this library by constructing a face detection classifier. Our protocols are efficient, taking milliseconds to a few seconds to perform a classification when running on real medical datasets.