Privacy-preserving deep learning with homomorphic encryption (HE) is a novel and promising research area aimed at designing deep learning solutions that operate while guaranteeing the privacy of user data. Designing privacy-preserving deep learning solutions requires one to completely rethink and redesign deep learning models and algorithms to match the severe technological and algorithmic constraints of HE. This paper provides an introduction to this complex research area as well as a methodology for designing privacy-preserving convolutional neural networks (CNNs). This methodology was applied to the design of a privacy-preserving version of the well-known LeNet-1 CNN, which was successfully operated on two benchmark datasets for image classification. Furthermore, this paper details and comments on the research challenges and software resources available for privacy-preserving deep learning with HE.

中文翻译：

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使用同态加密保护隐私的深度学习：简介


使用同态加密（HE）保护隐私的深度学习是一个新颖且有前途的研究领域，旨在设计在保证用户数据隐私的同时运行的深度学习解决方案。设计保护隐私的深度学习解决方案需要彻底重新思考和设计深度学习模型和算法，以适应高等教育严格的技术和算法限制。本文介绍了这一复杂的研究领域以及设计隐私保护卷积神经网络（CNN）的方法。该方法被应用于著名的 LeNet-1 CNN 的隐私保护版本的设计，该版本在两个图像分类基准数据集上成功运行。此外，本文还详细介绍了 HE 隐私保护深度学习的研究挑战和可用的软件资源，并进行了评论。