We present an extended abstract of MP2ML, a machine learning framework which integrates Intel nGraph-HE, a homomorphic encryption (HE) framework, and the secure two-party computation framework ABY, to enable data scientists to perform private inference of deep learning (DL) models trained using popular frameworks such as TensorFlow at the push of a button. We benchmark MP2ML on the CryptoNets network with ReLU activations, on which it achieves a throughput of 33.3 images/s and an accuracy of 98.6%. This throughput matches the previous state-of-the-art frameworks.
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MP2ML: A Mixed-Protocol Machine Learning Framework for Private Inference
Semantic Scholar · Computer Science · 2020
Abstract
We present an extended abstract of MP2ML, a machine learning framework which integrates Intel nGraph-HE, a homomorphic encryption (HE) framework, and the secure two-party computation framework ABY, to enable data scientists to perform private inference of deep learning (DL) models trained using popular frameworks such as TensorFlow at the push of a button. We benchmark MP2ML on the CryptoNets network with ReLU activations, on which it achieves a throughput of 33.3 images/s and an accuracy of 98.6%. This throughput matches the previous state-of-the-art frameworks.