Visa Inc.
PRIVACY-PRESERVING MACHINE LEARNING IN THE THREE-SERVER MODEL

Abstract:

Methods and systems according to embodiments of the invention provide for a framework for privacy-preserving machine learning which can be used to obtain solutions for training linear regression, logistic regression and neural network models. Embodiments of the invention are in a three-server model, wherein data owners secret-share their data among three servers who train and evaluate models on the joint data using three-party computation (3PC). Embodiments of the invention provide for efficient conversions between arithmetic, binary, and Yao 3PC, as well as techniques for fixed-point multiplication and truncation of shared decimal values. Embodiments also provide customized protocols for evaluating polynomial piecewise functions and a three-party oblivious transfer protocol.