Cryptology ePrint Archive: Report 2014/331
Machine Learning Classification over Encrypted Data
Raphael Bost and Raluca Ada Popa and Stephen Tu and Shafi Goldwasser
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, Na\"ive 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.
Category / Keywords: cryptographic protocols / public-key cryptography, implementation, applications, machine learning
Original Publication (with major differences): NDSS 2015
Date: received 11 May 2014, last revised 12 Jan 2015
Contact author: raphael_bost at alumni brown edu
Available format(s): PDF | BibTeX Citation
Version: 20150112:190551 (All versions of this report)
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