CRYPTOGRU: Low Latency Privacy-Preserving Text Analysis With GRU

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, CRYPTOGRU, for low-latency secure inferences. CRYPTOGRU 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 CRYP-TOGRU with multiple GRU models trained on 4 public datasets. Experimental results show CRYPTOGRU 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.