同态加密在深度学习中的应用综述

Survey on Application of Homomorphic Encryption in Deep Learning

随着深度学习在各种领域中的广泛应用,数据隐私和安全性问题变得日益重要。同态加密作为一种能够在加密数据上直接进行计算的加密技术,为解决这一问题提供了可能的解决方案。综述了深度学习与同态加密结合的方法,探讨了如何在加密环境中有效应用深度学习模型。介绍了同态加密技术的基础知识,涵盖了其基本原理、不同分类(部分同态加密、有限同态加密、全同态加密)以及全同态加密的发展历程。详细介绍了深度学习中的关键模型,包括卷积神经网络和Transformer模型。探讨了同态加密与深度学习结合的步骤以及如何将深度学习的各个层(如卷积层、注意力层、激活函数层)适配于同态加密环境。重点综述了现有的将卷积神经网络和Transformer与同态加密结合的具体方法,探讨了在加密数据上进行深度学习计算的实现方案以及为了提升效率和精度而采用的性能优化策略,并总结了每种方法的优势和局限性。总结了当前研究的进展,并对未来的研究方向进行了展望。

With the widespread application of deep learning in various fields,data privacy and security issues have become increasingly important.Homomorphic encryption,a technique that allows computations to be performed directly on encrypted data,offers a potential solution to these problems.This paper surveys methods that combine deep learning with homomorphic encryption,exploring how to effectively apply deep learning models in encrypted environments.Firstly,the basics of homomorphic encryption are introduced,covering its basic principles,different classifications(including partially homomorphic encryption,somewhat homomorphic encryption and fully homomorphic encryption),and the development history of fully homomorphic encryption.Key models in deep learning,such as convolutional neural network and Transformer,are then detailed.The steps of combining homomorphic encryption with deep learning and how to adapt various layers of deep learning(e.g.,convolutional layers,attention layer and activation function layer)to the homomorphic encryption environments are discussed.Subsequently,existing methods that integrate convolutional neural network and Transformer with homomorphic encryption are focused on.Specific implementation schemes for performing deep learning computations on encrypted data and performance optimization strategies employed to enhance efficiency and accuracy are discussed.The advantages and limitations of each method are summarized.Finally,current research progress is summarized,and an outlook on future research directions is provided.