# Encrypted Spiking Neural Networks Based on Adaptive Differential Privacy Mechanism

## TL;DR

This paper introduces an adaptive differential privacy method for spiking neural networks that improves accuracy while preserving privacy.

## Contribution

The novel ADPSNN dynamically adjusts privacy budgets based on spike-label correlations to enhance model utility.

## Key findings

- ADPSNN improves accuracy by 0.09% to 3.1% compared to existing methods.
- LIF neurons outperform on MNIST and Fashion-MNIST, while IF neurons excel on CIFAR datasets.
- The method shows potential for applications in image classification and healthcare.

## Abstract

Spike neural networks (SNNs) perform excellently in various domains. However, SNNs based on differential privacy (DP) protocols introduce uniform noise to the gradient parameters, which may affect the trade-off between model efficiency and personal privacy. Therefore, the adaptive differential private SNN (ADPSNN) is proposed in this work. It dynamically adjusts the privacy budget based on the correlations between the output spikes and labels. In addition, the noise is added to the gradient parameters according to the privacy budget. The ADPSNN is tested on four datasets with different spiking neurons including leaky integrated-and-firing (LIF) and integrate-and-fire (IF) models. Experimental results show that the LIF neuron model provides superior utility on the MNIST (accuracy 99.56%) and Fashion-MNIST (accuracy 92.26%) datasets, while the IF neuron model performs well on the CIFAR10 (accuracy 90.67%) and CIFAR100 (accuracy 66.10%) datasets. Compared to existing methods, the accuracy of ADPSNN is improved by 0.09% to 3.1%. The ADPSNN has many potential applications, such as image classification, health care, and intelligent driving.