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Recently, deep learning, which uses Deep Neural Networks (DNN), plays animportant role in many fields. A secure neural network model with a securetraining/inference scheme is indispensable to many applications. To accomplishsuch a task usually needs one of the entities (the customer or the serviceprovider) to provide private information (customer's data or the model) to theother. Without a secure scheme and the mutual trust between the serviceproviders and their customers, it will be an impossible mission. In this paper,we propose a novel privacy-preserving deep learning model and a securetraining/inference scheme to protect the input, the output, and the model inthe application of the neural network. We utilize the innate properties of adeep neural network to design a secure mechanism without using any complicatedcryptography component. The security analysis shows our proposed scheme issecure and the experimental results also demonstrate that our method is veryefficient and suitable for real applications.

A Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography Component