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.

本研究综述了利用同态加密解决神经网络数据隐私和安全问题的技术和策略，并分析了同态加密在神经网络训练和分类方面的当前研究现状、分类以及优化加密模型准确性和效率的技术。评估结果表明，尽管同态加密可以为神经网络提供强大的数据隐私保证，但仍存在许多挑战需要解决，例如对高级神经网络运算的有限支持、可扩展性问题以及性能平衡问题。