178 citations · 202 across the 2 of their papers we have counts for
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cs.CR2019
nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data
Fabian Boemer, Anamaria Costache, Rosario Cammarota +1
In previous work, Boemer et al. introduced nGraph-HE, an extension to the Intel nGraph deep learning (DL) compiler, that enables data scientists to deploy models with popular frame…
cs.CR2018
nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data
Fabian Boemer, Yixing Lao, Rosario Cammarota +1
Homomorphic encryption (HE)---the ability to perform computation on encrypted data---is an attractive remedy to increasing concerns about data privacy in deep learning (DL). Howeve…