NeurIPS 2020poster110 citations
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
Qian Lou, Bo Feng, Geoffrey Charles Fox, Lei Jiang
Abstract
Because of the lack of expertise, to gain benefits from their data, average users have to upload their private data to cloud servers they may not trust. Due to legal or privacy constraints, most users are willing to contribute only their encrypted data, and lack interests or resources to join deep neural network (DNN) training in cloud. To train a DNN on encrypted data in a completely non-interactive way, a recent work proposes a fully homomorphic encryption (FHE)-based technique implementing all activations by \textit{Brakerski-Gentry-Vaikuntanathan} (BGV)-based lookup tables. However, such inefficient lookup-table-based activations significantly prolong private training latency of DNNs.
BibTeX
@inproceedings{NEURIPS2020_685ac8ca,
author = {Lou, Qian and Feng, Bo and Charles Fox, Geoffrey and Jiang, Lei},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {9193--9202},
publisher = {Curran Associates, Inc.},
title = {Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/685ac8cadc1be5ac98da9556bc1c8d9e-Paper.pdf},
volume = {33},
year = {2020}
}