MP2ML
Fabian Boemer, Rosario Cammarota, Daniel Demmler, Thomas Schneider, Hossein Yalame
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.
Topics & Concepts
Computer scienceHomomorphic encryptionBenchmark (surveying)ThroughputInferenceArtificial intelligenceComputationDeep learningEncryptionMachine learningTheoretical computer scienceProgramming languageOperating systemGeodesyGeographyWirelessCryptography and Data SecurityPrivacy-Preserving Technologies in DataComplexity and Algorithms in Graphs