Publikationen

Autor	Boemer, Fabian; Cammarota, Rosario; Demmler, Daniel; Schneider, Thomas; Yalame, Hossein
Datum	2020
Art	Conference Proceedings
Abstrakt	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.
Konferenz	CCS '20: 2020 ACM SIGSAC Conference on Computer and Communications Security
ISBN	978-1-4503-8088-1
In	PPMLP'20: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice, p.43-45
Publisher	ACM
Url	https://tubiblio.ulb.tu-darmstadt.de/id/eprint/122329

MP2ML: A Mixed-Protocol Machine Learning Framework for Private Inference