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的关键问题，当前隐私保护方法通常与基于实数值的传统人工神经网络结合。作为新一代人工神经网络，脉冲神经网络（SNN）在众多领域发挥着重要作用。因此，亟需开展SNN隐私保护研究。本文将差分隐私（DP）算法与SNN相结合，提出差分隐私脉冲神经网络（DPSNN）。SNN利用离散脉冲序列传递信息，结合DP引入的梯度噪声，使得SNN保持较强的隐私保护能力。同时，为使SNN在获得高隐私保护的同时保持高性能，我们提出时序增强池化（TEP）方法，该方法将SNN中的时序信息充分融入空间信息传递过程，从而增强SNN的信息传递效果。我们在静态数据集与神经形态数据集上开展实验，结果表明，本算法在提供强隐私保护的同时仍能保持高性能。