Privacy protection is a crucial issue in machine learning algorithms, and the current privacy protection is combined with traditional artificial neural networks based on real values. Spiking neural network (SNN), the new generation of artificial neural networks, plays a crucial role in many fields. Therefore, research on the privacy protection of SNN is urgently needed. This paper combines the differential privacy(DP) algorithm with SNN and proposes a differentially private spiking neural network (DPSNN). The SNN uses discrete spike sequences to transmit information, combined with the gradient noise introduced by DP so that SNN maintains strong privacy protection. At the same time, to make SNN maintain high performance while obtaining high privacy protection, we propose the temporal enhanced pooling (TEP) method. It fully integrates the temporal information of SNN into the spatial information transfer, which enables SNN to perform better information transfer. We conduct experiments on static and neuromorphic datasets, and the experimental results show that our algorithm still maintains high performance while providing strong privacy protection.

翻译：隐私保护是机器学习算法中关键性的问题，目前的隐私保护与基于实值的传统人工神经网络相结合。脉冲神经网络(SNN)作为新一代人工神经网络，在许多领域发挥着至关重要的作用。因此，迫切需要研究SNN的隐私保护。本文将差分隐私算法与SNN相结合，提出了一种差分隐私脉冲神经网络(DPSNN)。SNN使用离散脉冲序列传输信息，结合由DP引入的梯度噪声，使SNN保持强隐私保护。同时，为使SNN在获得高隐私保护的同时仍然保持高性能，我们提出了时间增强池化(TEP)方法。该方法将SNN的时间信息完全整合到空间信息传输中，使SNN能够更好地传输信息。我们在静态和神经型数据集上进行了实验，实验结果表明，我们的算法在提供强隐私保护的同时仍然保持高性能。