Paper 2019/140 Original: eprint.iacr.org/2019/140 ↗
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning
Abstract
How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML keeps both the data and the model information-theoretically private, while allowing efficient parallelization of training across distributed workers. We characterize CodedPrivateML's privacy threshold and prove its convergence for logistic (and linear) regression. Furthermore, via experiments over Amazon EC2, we demonstrate that CodedPrivateML can provide an order of magnitude speedup (up to $\sim 34\times$) over the state-of-the-art cryptographic approaches.
Metadata
- Available format(s)
PDF- Category
- Applications
- Publication info
- Preprint.
- History
- 2019-02-14: last updated
- 2019-02-14: published
- Short URL
- https://ia.cr/2019/140
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2019/140,
author = {Jinhyun So and Basak Guler and A. Salman Avestimehr and Payman Mohassel},
title = {CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning},
howpublished = {Cryptology {ePrint} Archive, Paper 2019/140},
year = {2019},
url = {https://eprint.iacr.org/2019/140}
}