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