  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.

Neural and Evolutionary Computing;Artificial Intelligence;Machine Learning