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Privacy-preserving distributed linear regression on high-dimensional data
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Gascon, Adrià, Schoppmann, Phillipp, Balle, Borja, Raykova, Mariana, Doerner, Jack, Zahur, Samee and Evans, David (2017) Privacy-preserving distributed linear regression on high-dimensional data. Proceedings on Privacy Enhancing Technologies, 2017 (4). pp. 345-364. doi:10.1515/popets-2017-0053
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Official URL: https://doi.org/10.1515/popets-2017-0053
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.
Item Type: | Journal Article | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Journal or Publication Title: | Proceedings on Privacy Enhancing Technologies | ||||||
Publisher: | De Gruyter | ||||||
Official Date: | 10 October 2017 | ||||||
Dates: |
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Volume: | 2017 | ||||||
Number: | 4 | ||||||
Page Range: | pp. 345-364 | ||||||
DOI: | 10.1515/popets-2017-0053 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) |
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