@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://preview.aclanthology.org/jlcl-multiple-ingestion/2021.emnlp-main.156/",
    doi = "10.18653/v1/2021.emnlp-main.156",
    pages = "2052--2057",
    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 $138\times$ over one of the state-of-the-art secure networks on the Penn Treebank dataset."
}