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We propose AriaNN, a low-interaction privacy-preserving framework for privateneural network training and inference on sensitive data. Our semi-honest2-party computation protocol (with a trusted dealer) leverages function secretsharing, a recent lightweight cryptographic protocol that allows us to achievean efficient online phase. We design optimized primitives for the buildingblocks of neural networks such as ReLU, MaxPool and BatchNorm. For instance, weperform private comparison for ReLU operations with a single message of thesize of the input during the online phase, and with preprocessing keys close to4X smaller than previous work. Last, we propose an extension to support n-partyprivate federated learning. We implement our framework as an extensible systemon top of PyTorch that leverages CPU and GPU hardware acceleration forcryptographic and machine learning operations. We evaluate our end-to-endsystem for private inference between distant servers on standard neuralnetworks such as AlexNet, VGG16 or ResNet18, and for private training onsmaller networks like LeNet. We show that computation rather than communicationis the main bottleneck and that using GPUs together with reduced key size is apromising solution to overcome this barrier.

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