In the era of big data, users pay more attention to data privacy issues in many application
fields, such as healthcare, finance, and so on. However, in the current application scenarios …

Secure neural network inference provides a promising solution to preserve the privacy of
Deep Learning as a Service (DLaaS), but its substantial communication and computation …

Neural Networks (NN) provide a powerful method for machine learning training and
inference. To effectively train, it is desirable for multiple parties to combine their data …

Neural Networks (NN) provide a powerful method for machine learning training and
inference. To effectively train, it is desirable for multiple parties to combine their data …

Secure neural network inference has been a promising solution to private Deep-Learning-as-
a-Service, which enables the service provider and user to execute neural network inference …

Well-trained neural network models are deployed on edge servers to provide valuable
inference services for clients. To protect data privacy, a promising way is to exploit various …

Privacy-preserving neural network based on secure multi-party computation (MPC) enables
multiple parties to jointly train neural network models without revealing sensitive data. In …

Convolutional neural network (CNN) is one of the representative models of deep learning,
commonly used to analyze visual images. CNN model is more accurate when trained on …

The rise of cloud computing technology led to a paradigm shift in technological services that
enabled enterprises to delegate their data analytics tasks to cloud servers which have …

We propose AriaNN, a low-interaction privacy-preserving framework for private neural
network training and inference on sensitive data. Our semi-honest 2-party computation …