The rapid development of artificial intelligence has brought considerable convenience, yet also introduces significant security risks. One of the research hotspots is to balance data privacy and utility in the real world of artificial intelligence. The present second-generation artificial neural networks have made tremendous advances, but some big models could have really high computational costs. The third-generation neural network, SNN (Spiking Neural Network), mimics real neurons by using discrete spike signals, whose sequences exhibit strong sparsity, providing advantages such as low energy consumption and high efficiency. In this paper, we construct a framework to evaluate the homomorphic computation of SNN named FHE-DiSNN that enables SNN to achieve good prediction performance on encrypted data. First, benefitting from the discrete nature of spike signals, our proposed model avoids the errors introduced by discretizing activation functions. Second, by applying bootstrapping, we design new private preserving functions FHE-Fire and FHE-Reset, through which noise can be refreshed, allowing us to evaluate SNN for an arbitrary number of operations. Furthermore, We improve the computational efficiency of FHE-DiSNN while maintaining a high level of accuracy. Finally, we evaluate our model on the MNIST dataset. The experiments show that FHE-DiSNN with 30 neurons in the hidden layer achieves a minimum prediction accuracy of 94.4%. Under optimal parameters, it achieves a 95.1% accuracy, with only a 0.6% decrease compared to the original SNN (95.7%). These results demonstrate the superiority of SNN over second-generation neural networks for homomorphic evaluation.

翻译：人工智能的快速发展带来了极大的便利，但也引入了显著的安全风险。研究热点之一是在现实人工智能应用中平衡数据隐私与实用性。当前第二代人工神经网络取得了巨大进步，但某些大模型可能产生极高的计算成本。第三代神经网络SNN（脉冲神经网络）通过使用离散脉冲信号模拟真实神经元，其序列表现出强稀疏性，从而具有低能耗、高效率等优势。本文构建了一个评估SNN同态计算的框架FHE-DiSNN，使SNN能够在加密数据上实现良好的预测性能。首先，得益于脉冲信号的离散特性，所提模型避免了激活函数离散化引入的误差。其次，通过应用自举技术，我们设计了新的隐私保护函数FHE-Fire和FHE-Reset，利用这些函数可刷新噪声，从而实现对任意运算步数的SNN评估。此外，我们在保持高精度的同时提升了FHE-DiSNN的计算效率。最后，我们在MNIST数据集上评估了模型。实验表明，隐藏层含30个神经元的FHE-DiSNN最低预测准确率达94.4%。在最优参数下，其准确率达到95.1%，相比原始SNN（95.7%）仅下降0.6%。这些结果证明了SNN在同态评估方面优于第二代神经网络。