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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