Privacy-Preserving Distributed Linear Regression on High‑Dimensional Data

Adrià Gascón and Phillipp Schoppmann and Borja Balle and Mariana Raykova and Jack Doerner and Samee Zahur and David Evans
Privacy Enhancing Technologies Symposium (PETS)
Minneapolis, Minnesota
18 – 21 July 2017

Abstract

We propose privacy-preserving protocols for computing linear regression models, in the setting where the training dataset is vertically distributed among several parties. Our main contribution is a hybrid multi-party computation protocol that combines Yao's garbled circuits with tailored protocols for computing inner products. Like many machine learning tasks, building a linear regression model involves solving a system of linear equations. We conduct a comprehensive evaluation and comparison of different techniques for securely performing this task, including a new Conjugate Gradient Descent (CGD) algorithm. This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Our technique improves on Nikolaenko et al.'s method for privacy-preserving ridge regression (S&P 2013), and can be used as a building block in other analyses. We implement a complete system and demonstrate that our approach is highly scalable, solving data analysis problems with one million records and one hundred features in less than one hour of total running time.

Project Site

OblivC.org

Paper

[PDF, 20 pages] (Cryptology ePrint Report 2016/892) (PoPETs, Volume 2017, Issue 4)
CS 655 David Evans - Publications
University of Virginia
Department of Computer Science
David Evans
evans@cs.virginia.edu