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ML Confidential: Machine Learning on Encrypted Data

Thore Graepel, Kristin Lauter, and Michael Naehrig

Abstract

We demonstrate that, by using a recently proposed leveled homomorphic encryption scheme, it is possible to delegate the execution of a machine learning algorithm to a computing service while retaining confidentiality of the training and test data. Since the computational complexity of the homomorphic encryption scheme depends primarily on the number of levels of multiplications to be carried out on the encrypted data, we define a new class of machine learning algorithms in which the algorithm's predictions, viewed as functions of the input data, can be expressed as polynomials of bounded degree. We propose confidential algorithms for binary classification based on polynomial approximations to least-squares solutions obtained by a small number of gradient descent steps. We present experimental validation of the confidential machine learning pipeline and discuss the trade-offs regarding computational complexity, prediction accuracy and cryptographic security.

Details

Publication typeInproceedings
Published inInternational Conference on Information Security and Cryptology – ICISC 2012, Lecture Notes in Computer Science, to appear
PublisherSpringer Verlag
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