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Secure Multi-Party Linear Regression on High-Dimensional Data

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The goal of secure multi-pary computation (MPC) is to facilitate the evaluation of functionalities that depend on the private inputs of several distrusting parties in a privacy preserving manner. I will start my talk by discussing potential applications of secure MPC to machine learning and the relation between MPC and other well-known privacy frameworks like differential privacy. Then I will present our recent work on secure MPC protocols for linear regression on distributed databases. By combining several tools from the MPC literature we obtain scalable solutions that can solve problems with millions of records and hundreds of features in a matter of minutes. Some crucial implementation details will be discussed, including the role of fixed-point arithmetic and a robust conjugate gradient descent solver for private linear systems. An implementation of our protocols based on the Obliv-C framework is available as open source.

Bio: Borja Balle is currently a Machine Learning Scientist at Amazon Research Cambridge. Before joining Amazon, Borja was a lecturer at Lancaster University (2015-2017) and a postdoctoral fellow at McGill University (2013-2015). His main research interest is in privacy-preserving machine learning, including the use of differential privacy and multi-party computation in distributed learning problems, and the foundations of privacy-aware data science. More info at https://borjaballe.github.io

This talk is part of the Computer Laboratory Systems Research Group Seminar series.

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