CRYPTOGRU: Low Latency Privacy-Preserving Text Analysis With GRU
Bo Feng, Qian Lou, Lei Jiang, Geoffrey Fox
Abstract
Homomorphic encryption (HE) and garbled circuit (GC) provide the protection for users’ privacy. However, simply mixing the HE and GC in RNN models suffer from long inference latency due to slow activation functions. In this paper, we present a novel hybrid structure of HE and GC gated recurrent unit (GRU) network, , for low-latency secure inferences. replaces computationally expensive GC-based tanh with fast GC-based ReLU, and then quantizes sigmoid and ReLU to smaller bit-length to accelerate activations in a GRU. We evaluate with multiple GRU models trained on 4 public datasets. Experimental results show achieves top-notch accuracy and improves the secure inference latency by up to 138× over one of the state-of-the-art secure networks on the Penn Treebank dataset.
BibTeX
@inproceedings{feng-etal-2021-cryptogru,
title = "{CRYPTOGRU}: Low Latency Privacy-Preserving Text Analysis With {GRU}",
author = "Feng, Bo and
Lou, Qian and
Jiang, Lei and
Fox, Geoffrey",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.156/",
doi = "10.18653/v1/2021.emnlp-main.156",
pages = "2052--2057"
}