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Privacy-Preserving Ridge Regression on Hundreds of Millions of Records
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BibTeX
@MISC{Nikolaenko_privacy-preservingridge,
author = {Valeria Nikolaenko and Udi Weinsberg and Stratis Ioannidis and Marc Joye and Dan Boneh and Nina Taft},
title = {Privacy-Preserving Ridge Regression on Hundreds of Millions of Records},
year = {}
}
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Abstract
Abstract—Ridge regression is an algorithm that takes as input a large number of data points and finds the best-fit linear curve through these points. The algorithm is a building block for many machine-learning operations. We present a system for privacy-preserving ridge regression. The system outputs the best-fit curve in the clear, but exposes no other information about the input data. Our approach combines both homomorphic encryption and Yao garbled circuits, where each is used in a different part of the algorithm to obtain the best performance. We implement the complete system and experiment with it on real data-sets, and show that it significantly outperforms pure implementations based only on homomorphic encryption or Yao circuits. x1,y1 x x2,y2
Keyphrases
privacy-preserving ridge regression homomorphic encryption data point input data approach combine building block pure implementation complete system best-fit curve best-fit linear curve large number different part yao circuit real data-sets many machine-learning operation abstract ridge regression
