DPSNN: A Differentially Private Spiking Neural Network with Temporal Enhanced Pooling
This addresses privacy concerns for SNN applications in fields like neuromorphic computing, but it appears incremental as it adapts existing differential privacy techniques to a new neural network type.
The paper tackles the problem of privacy protection in spiking neural networks (SNNs) by combining differential privacy with SNNs, proposing DPSNN and a temporal enhanced pooling method to maintain high performance. Experimental results on static and neuromorphic datasets show the algorithm provides strong privacy protection while maintaining high performance, though no concrete numbers are specified.
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.