The use of Neural Networks (NNs) for sensitive data processing is becoming increasingly popular, raising concerns about data privacy and security. Homomorphic Encryption (HE) has the potential to be used as a solution to preserve data privacy in NN. This study provides a comprehensive analysis on the use of HE for NN training and classification, focusing on the techniques and strategies used to enhance data privacy and security. The current state-of-the-art in HE for NNs is analysed, and the challenges and limitations that need to be addressed to make it a reliable and efficient approach for privacy preservation are identified. Also, the different categories of HE schemes and their suitability for NNs are discussed, as well as the techniques used to optimize the accuracy and efficiency of encrypted models. The review reveals that HE has the potential to provide strong data privacy guarantees for NNs, but several challenges need to be addressed, such as limited support for advanced NN operations, scalability issues, and performance trade-offs.

翻译：摘要：神经网络在处理敏感数据中的应用日益普及，由此引发了对数据隐私与安全性的关切。同态加密作为一种潜在解决方案，可用于保障神经网络中的数据隐私。本研究系统分析了同态加密在神经网络训练与分类中的应用，重点探讨了增强数据隐私与安全性的技术与策略。通过评估当前同态加密在神经网络领域的最新技术水平，识别了将其发展为可靠高效的隐私保护方法所需解决的关键挑战与局限性。文章进一步讨论了不同类别的同态加密方案及其对神经网络的适用性，以及优化加密模型精度与效率的技术手段。综述表明，同态加密虽能为神经网络提供强大的数据隐私保障，但仍需应对高级神经网络操作支持有限、可扩展性问题及性能权衡等若干挑战。