With the rapid development of AI technology, we have witnessed numerous innovations and conveniences. However, along with these advancements come privacy threats and risks. Fully Homomorphic Encryption (FHE) emerges as a key technology for privacy-preserving computation, enabling computations while maintaining data privacy. Nevertheless, FHE has limitations in processing continuous non-polynomial functions as it is restricted to discrete integers and supports only addition and multiplication. Spiking Neural Networks (SNNs) operate on discrete spike signals, naturally aligning with the properties of FHE. In this paper, we present a framework called FHE-DiCSNN. This framework is based on the efficient TFHE scheme and leverages the discrete properties of SNNs to achieve high prediction performance on ciphertexts. Firstly, by employing bootstrapping techniques, we successfully implement computations of the Leaky Integrate-and-Fire neuron model on ciphertexts. Through bootstrapping, we can facilitate computations for SNNs of arbitrary depth. This framework can be extended to other spiking neuron models, providing a novel framework for the homomorphic evaluation of SNNs. Secondly, inspired by CNNs, we adopt convolutional methods to replace Poisson encoding. This not only enhances accuracy but also mitigates the issue of prolonged simulation time caused by random encoding. Furthermore, we employ engineering techniques to parallelize the computation of bootstrapping, resulting in a significant improvement in computational efficiency. Finally, we evaluate our model on the MNIST dataset. Experimental results demonstrate that, with the optimal parameter configuration, FHE-DiCSNN achieves an accuracy of 97.94% on ciphertexts, with a loss of only 0.53% compared to the original network's accuracy of 98.47%. Moreover, each prediction requires only 0.75 seconds of computation time

翻译：随着人工智能技术的迅速发展，我们见证了众多创新与便利。然而，这些进步也带来了隐私威胁与风险。全同态加密(FHE)作为隐私保护计算的关键技术，能够在保障数据隐私的同时实现计算。然而，FHE受限于离散整数且仅支持加法与乘法，因此在处理连续非多项式函数方面存在局限。脉冲神经网络(SNN)基于离散脉冲信号工作，天然契合FHE的特性。本文提出一种名为FHE-DiCSNN的框架，该框架基于高效的TFHE方案，利用SNN的离散特性在密文上实现高预测性能。首先，通过自举技术，我们成功实现了泄漏积分点火神经元模型在密文上的计算。借助自举技术，我们能够支持任意深度SNN的计算，且该框架可扩展至其他脉冲神经元模型，为SNN的同态评估提供了新型框架。其次，受CNN启发，我们采用卷积方法替代泊松编码。这不仅提升了精度，还缓解了随机编码导致的模拟时间过长问题。此外，我们采用工程化技术对自举计算进行并行化处理，显著提高了计算效率。最后，我们在MNIST数据集上评估模型。实验结果表明，在最优参数配置下，FHE-DiCSNN在密文上达到97.94%的准确率，仅比原始网络98.47%的准确率下降0.53%，且每次预测仅需0.75秒的计算时间。