Paper 2020/1577
Multi-Party Replicated Secret Sharing over a Ring with Applications to Privacy-Preserving Machine Learning
Abstract
Secure multi-party computation has seen significant performance advances and increasing use in recent years. Techniques based on secret sharing offer attractive performance and are a popular choice for privacy-preserving machine learning applications. Traditional techniques operate over a field, while designing equivalent techniques for a ring $\mathbb{Z}_{2^k}$ can boost performance. In this work, we develop a suite of multi-party protocols for a ring in the honest majority setting starting from elementary operations to more complex with the goal of supporting general-purpose computation. We demonstrate that our techniques are substantially faster than their field-based equivalents when instantiated with a different number of parties and perform on par with or better than state-of-the-art techniques with designs customized for a fixed number of parties. We evaluate our techniques on machine learning applications and show that they offer attractive performance.
Note: This version contains corrected 5pc and 7pc LAN/WAN experimental results.
Metadata
- Available format(s)
-
PDF
- Category
- Cryptographic protocols
- Publication info
- Published elsewhere. Privacy Enhancing Technologies Symposium
- Keywords
- secure multi-party computationsecret sharing
- Contact author(s)
-
anbaccar @ buffalo edu
mblanton @ buffalo edu
chyuan @ buffalo edu - History
- 2024-03-19: last of 4 revisions
- 2020-12-21: received
- See all versions
- Short URL
- https://ia.cr/2020/1577
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2020/1577,
author = {Alessandro Baccarini and Marina Blanton and Chen Yuan},
title = {Multi-Party Replicated Secret Sharing over a Ring with Applications to Privacy-Preserving Machine Learning},
howpublished = {Cryptology {ePrint} Archive, Paper 2020/1577},
year = {2020},
url = {https://eprint.iacr.org/2020/1577}
}
