Paper 2017/762
Private Collaborative Neural Network Learning
Melissa Chase, Ran Gilad-Bachrach, Kim Laine, Kristin Lauter, and Peter Rindal
Abstract
Machine learning algorithms, such as neural networks, create better predictive models when having access to larger datasets. In many domains, such as medicine and finance, each institute has only access to limited amounts of data, and creating larger datasets typically requires collaboration. However, there are privacy related constraints on these collaborations for legal, ethical, and competitive reasons. In this work, we present a feasible protocol for learning neural networks in a collaborative way while preserving the privacy of each record. This is achieved by combining Differential Privacy and Secure Multi-Party Computation with Machine Learning.
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Preprint. MINOR revision.
- Keywords
- deep learningneural networksdifferential privacysecure multi-party computation
- Contact author(s)
- kim laine @ microsoft com
- History
- 2017-08-08: received
- Short URL
- https://ia.cr/2017/762
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2017/762,
author = {Melissa Chase and Ran Gilad-Bachrach and Kim Laine and Kristin Lauter and Peter Rindal},
title = {Private Collaborative Neural Network Learning},
howpublished = {Cryptology {ePrint} Archive, Paper 2017/762},
year = {2017},
url = {https://eprint.iacr.org/2017/762}
}
