Paper 2019/338
Garbled Neural Networks are Practical
Marshall Ball, Brent Carmer, Tal Malkin, Mike Rosulek, and Nichole Schimanski
Abstract
We show that garbled circuits are a practical choice for secure evaluation of neural network classifiers. At the protocol level, we start with the garbling scheme of Ball, Malkin & Rosulek (ACM CCS 2016) for arithmetic circuits and introduce new optimizations for modern neural network activation functions. We develop fancy-garbling, the first implementation of the BMR16 garbling scheme along with our new optimizations, as part of heavily optimized garbled-circuits tool that is driven by a TensorFlow classifier description. We evaluate our constructions on a wide range of neural networks. We find that our approach is up to 100x more efficient than straight-forward boolean garbling (depending on the neural network). Our approach is also roughly 40% more efficient than DeepSecure (Rouhani et al., DAC 2018), the only previous garbled-circuit-based approach for secure neural network evaluation, which incorporates significant optimization techniques for boolean circuits. Furthermore, our approach is competitive with other non-garbled-circuit approaches for secure neural network evaluation.
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Preprint. MINOR revision.
- Keywords
- garbled circuitsneural networks
- Contact author(s)
- bcarmer @ gmail com
- History
- 2019-06-24: last of 2 revisions
- 2019-04-03: received
- See all versions
- Short URL
- https://ia.cr/2019/338
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2019/338,
author = {Marshall Ball and Brent Carmer and Tal Malkin and Mike Rosulek and Nichole Schimanski},
title = {Garbled Neural Networks are Practical},
howpublished = {Cryptology ePrint Archive, Paper 2019/338},
year = {2019},
note = {\url{https://eprint.iacr.org/2019/338}},
url = {https://eprint.iacr.org/2019/338}
}
