A novel privacy-preserving deep learning scheme without a cryptography component. (September 2021)

• Record Type: Journal Article
• Authors: Sun, Chin-Yu; Wu, Allen C.-H.; Hwang, TingTing
• Is Part Of: Computers & electrical engineering. Volume 94(2021)
• Publication Date: 2021-09
• Subjects: Deep learning -- Convolutional neural networks -- Privacy-preserving -- Model protection -- Security; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107325
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Recently, deep learning using Convolutional Neural Networks has played an essential role in many fields. Traditional cryptography, such as the technologies of the garbled circuit and the homomorphic encryption, may provide both parties with a private and secure computation in the neural networks as well as a secure inference scheme. However, it suffers heavy computation in practical designs especially for the training of a CNN model. Hence, the scalability of the model is restricted by these components. In this paper, we propose a novel deep learning model and a secure inferencing scheme in an application of a neural network. We utilize the inherent properties of a convolutional 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 demonstrate that our method is very efficient and suitable for practical applications.