Machine learning (ML) is widely used today, especially through deep neural networks (DNNs), however, increasing computational load and resource requirements have led to cloud-based solutions. To address this problem, a new generation of networks called Spiking Neural Networks (SNN) has emerged, which mimic the behavior of the human brain to improve efficiency and reduce energy consumption. These networks often process large amounts of sensitive information, such as confidential data, and thus privacy issues arise. Homomorphic encryption (HE) offers a solution, allowing calculations to be performed on encrypted data without decrypting it. This research compares traditional DNNs and SNNs using the Brakerski/Fan-Vercauteren (BFV) encryption scheme. The LeNet-5 model, a widely-used convolutional architecture, is used for both DNN and SNN models based on the LeNet-5 architecture, and the networks are trained and compared using the FashionMNIST dataset. The results show that SNNs using HE achieve up to 40% higher accuracy than DNNs for low values of the plaintext modulus t, although their execution time is longer due to their time-coding nature with multiple time-steps.

翻译：机器学习（ML）如今被广泛使用，尤其是通过深度神经网络（DNN），然而，不断增加的计算负荷和资源需求催生了基于云的解决方案。为解决这一问题，新一代名为脉冲神经网络（SNN）的网络应运而生，它模拟人脑行为以提高效率并降低能耗。这些网络常处理大量敏感信息（如机密数据），因此隐私问题随之产生。同态加密（HE）提供了一种解决方案，允许在不解密的情况下对加密数据执行计算。本研究采用Brakerski/Fan-Vercauteren（BFV）加密方案，对比了传统DNN与SNN。基于LeNet-5架构的LeNet-5模型（一种广泛使用的卷积架构）被同时用于DNN和SNN模型，并使用FashionMNIST数据集对网络进行训练与比较。结果表明，当明文模数t取值较低时，采用HE的SNN相较于DNN的准确率高出高达40%，尽管由于其时间编码特性（涉及多个时间步长），其执行时间更长。