Paper 2021/091 Original: eprint.iacr.org/2021/091 ↗
Programmable Bootstrapping Enables Efficient Homomorphic Inference of Deep Neural Networks
Abstract
In many cases, machine learning and privacy are perceived to be at odds. Privacy concerns are especially relevant when the involved data are sensitive. This paper deals with the privacy-preserving inference of deep neural networks. We report on first experiments with a new library implementing a variant of the TFHE fully homomorphic encryption scheme. The underlying key technology is the programmable bootstrapping. It enables the homomorphic evaluation of any function of a ciphertext, with a controlled level of noise. Our results indicate for the first time that deep neural networks are now within the reach of fully homomorphic encryption. Importantly, in contrast to prior works, our framework does not necessitate re-training the model.
Metadata
- Available format(s)
PDF- Category
- Applications
- Publication info
- Preprint.
- History
- 2021-11-25: last updated
- 2021-01-27: published
- Short URL
- https://ia.cr/2021/091
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2021/091,
author = {Ilaria Chillotti and Marc Joye and Pascal Paillier},
title = {Programmable Bootstrapping Enables Efficient Homomorphic Inference of Deep Neural Networks},
howpublished = {Cryptology {ePrint} Archive, Paper 2021/091},
year = {2021},
url = {https://eprint.iacr.org/2021/091}
}