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ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing

Théo Ryffel 1 David Pointcheval 2, 1 Francis Bach 2, 3
1 CASCADE - Construction and Analysis of Systems for Confidentiality and Authenticity of Data and Entities
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique : UMR 8548, Inria de Paris
3 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : We propose ARIANN, a low-interaction framework to perform private training and inference of standard deep neural networks on sensitive data. This framework implements semi-honest 2-party computation and leverages function secret sharing, a recent cryptographic protocol that only uses lightweight primitives to achieve an efficient online phase with a single message of the size of the inputs, for operations like comparison and multiplication which are building blocks of neural networks. Built on top of PyTorch, it offers a wide range of functions including ReLU, MaxPool and BatchNorm, and allows to use models like AlexNet or ResNet18. We report experimental results for inference and training over distant servers. Last, we propose an extension to support n-party private federated learning.
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https://hal.inria.fr/hal-02896127
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Submitted on : Friday, July 10, 2020 - 12:50:03 PM
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Théo Ryffel, David Pointcheval, Francis Bach. ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing. 2020. ⟨hal-02896127⟩

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