对于 Spiking 神经网络的 SpyKing-保护隐私的框架
Farzad Nikfam1, Alberto Marchisio2, Maurizio Martina1
1Very Large Scale Integration Laboratory, Department of Electronics Engineering, Politecnico di Torino, Torino, Italy.
Frontiers in neuroscience
|June 16, 2025
概括
完全同型加密 (FHE) 能够实现安全的AI计算. 尖端神经网络 (SNN) 显示出对保护隐私的人工智能的承诺,在加密数据上实现比深度神经网络更高的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 密码学 密码学 密码学 密码学
背景情况:
- 深度神经网络 (DNN) 广泛使用,但处理敏感数据,引发隐私问题.
- 完全同型加密 (FHE) 允许对加密数据进行计算,为保护隐私的人工智能提供解决方案.
- 尖端神经网络 (SNN) 模仿生物神经元,并可能在加密计算中提供优势.
研究的目的:
- 为了评估FHE对DNN应用的性能.
- 将FHE应用的DNN与保护隐私的人工智能的SNN进行比较.
- 分析加密神经计算的挑战,特别是非线性运算.
主要方法:
- 在MNIST,FashionMNIST和CIFAR10数据集上进行了实验.
- 使用SpyKing框架来分析加密的神经计算.
- 加密参数被系统地改变,以优化SNN的性能.
主要成果:
- FHE显著增加了计算成本,但保持了准确性和数据安全性.
- 在低纯文本模量值的加密数据上,SNN实现了高达35%的绝对精度,高于DNN.
- 确定了FHE在处理非线性操作中的局限性.
结论:
- FHE是一种可行的,尽管计算密集的,用于保护隐私的人工智能的技术.
- 在保护隐私的人工智能应用中,SNN显示了提高准确性的巨大潜力.
- 人们越来越需要安全和高效的神经计算解决方案.
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