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• W2885515512 abstract "In this paper, a secure Convolutional Neural Network classifier is proposed using Fully Homomorphic Encryption (FHE). The secure classifier provides a user with the ability to out-source the computations to a powerful cloud server and/or setup a server to classify inputs without providing the model or revealing source data. To this end, a real number framework is developed over FHE by using a fixed point format with binary digits. This allows for real number computations for basic operators like addition, subtraction, and multiplication but also to include secure comparisons and max functions. Additionally, a rectified linear unit is designed and realized in the framework. Experimentally, the model was verified using a Convolutional Neural Network trained for handwritten digits. This encrypted implementation shows accurate results for all classification when compared against an unencrypted implementation." @default.
• W2885515512 created "2018-08-22" @default.
• W2885515512 creator A5058258391 @default.
• W2885515512 creator A5075408415 @default.
• W2885515512 date "2018-08-11" @default.
• W2885515512 modified "2023-09-27" @default.
• W2885515512 title "Secure Convolutional Neural Network using FHE." @default.
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• W2885515512 hasPublicationYear "2018" @default.
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