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- W2969596416 abstract "Recently, deep learning, which uses Deep Neural Networks (DNN), plays an important role in many fields. A secure neural network model with a secure training/inference scheme is indispensable to many applications. To accomplish such a task usually needs one of the entities (the customer or the service provider) to provide private information (customer's data or the model) to the other. Without a secure scheme and the mutual trust between the service providers and their customers, it will be an impossible mission. In this paper, we propose a novel privacy-preserving deep learning model and a secure training/inference scheme to protect the input, the output, and the model in the application of the neural network. We utilize the innate properties of a deep neural network to design a secure mechanism without using any complicated cryptography component. The security analysis shows our proposed scheme is secure and the experimental results also demonstrate that our method is very efficient and suitable for real applications." @default.
- W2969596416 created "2019-08-29" @default.
- W2969596416 creator A5004270539 @default.
- W2969596416 creator A5043578401 @default.
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- W2969596416 date "2019-08-20" @default.
- W2969596416 modified "2023-10-16" @default.
- W2969596416 title "A Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography Component" @default.
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- W2969596416 doi "https://doi.org/10.48550/arxiv.1908.07701" @default.
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