Paper 2023/1219

A Note on “Secure Quantized Training for Deep Learning”

Marcel Keller, CSIRO's Data61
Ke Sun, CSIRO's Data61, Australian National University
Abstract

Keller and Sun (ICML'22) have found a gap in the accuracy between floating-point deep learning in cleartext and secure quantized deep learning using multi-party computation. We have discovered that this gap is caused by a bug in the implementation of max-pooling. In this note, we present updated figures to support this conclusion. We also add figures for another network on CIFAR-10.

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
Preprint.
Keywords
Privacy-preserving machine learningsecure multi-party computation
Contact author(s)
mks keller @ gmail com
ke sun @ data61 csiro au
History
2023-08-11: approved
2023-08-11: received
See all versions
Short URL
https://ia.cr/2023/1219
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2023/1219,
      author = {Marcel Keller and Ke Sun},
      title = {A Note on “Secure Quantized Training for Deep Learning”},
      howpublished = {Cryptology {ePrint} Archive, Paper 2023/1219},
      year = {2023},
      url = {https://eprint.iacr.org/2023/1219}
}
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