CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning

Jinhyun So; Basak Guler; A.  Salman Avestimehr; Payman Mohassel

Paper 2019/140

CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning

Jinhyun So, Basak Guler, A. Salman Avestimehr, and Payman Mohassel

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: Published elsewhere. arXiv:1902.00641
Keywords: privacy-preserving machine learning information-theoretic privacy
Contact author(s): jinhyuns @ usc edu
bguler @ usc edu
History: 2019-02-14: received
Short URL: https://ia.cr/2019/140
License: CC BY

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}
}