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）在当前被广泛应用，尤其是通过深度神经网络（DNN），然而，不断增长的计算负载和资源需求催生了基于云的解决方案。为解决这一问题，一类被称为脉冲神经网络（SNN）的新型网络应运而生，其通过模仿人脑行为来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），因此引发了隐私问题。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进行计算。本研究对比了采用Brakerski/Fan-Vercauteren（BFV）加密方案的传统DNN与SNN。基于LeNet-5架构（一种广泛使用的卷积架构）的LeNet-5模型被同时用于DNN和SNN模型，并利用FashionMNIST数据集对网络进行训练和比较。结果表明，在明文模数t取值较小时，使用HE的SNN比DNN的准确率最高可提升40%，尽管由于其时间编码特性需多个时间步长，其执行时间更长。