用加密技术保护脉冲神经网络推理隐私，兼顾效率与安全。
PrivSpike: Employing Homomorphic Encryption for Private Inference of Deep Spiking Neural Networks
- 基于CKKS加密方案，实现任意深度脉冲神经网络的私有推理。
- 在多个数据集上达到98.1%以上准确率，推理时间可接受。
- 适合需要高能效与强隐私保护的边缘计算场景。
深度学习依赖大规模数据和算力，但数据常含敏感信息，隐私问题突出。脉冲神经网络（SNN）虽能效更高，仍面临相同隐私挑战。本文提出 PRIVSPIKE 框架，利用 CKKS 同态加密实现 SNN 的私有推理，支持任意深度结构。提出两种关键算法：一是用于高效推理的漏电积分-放电激活函数多项式近似；二是精度优化的方案切换机制。在 MNIST、CIFAR-10、Neuromorphic MNIST 与 CIFAR-10 DVS 数据集上，分别实现 98.10%、79.3%、98.1% 和 66.0% 的加密推理准确率。在消费级 CPU 上，LeNet-5 模型推理时间为 28 秒（MNIST）与 212 秒（Neuromorphic MNIST），ResNet-19 模型为 784 秒（CIFAR-10）与 1846 秒（CIFAR-10 DVS）。结果表明，PRIVSPIKE 是安全且高效的 SNN 加密推理方案，优于现有方法。
原文摘要 · Abstract (English)
Deep learning has become a cornerstone of modern machine learning. It relies heavily on vast datasets and significant computational resources for high performance. This data often contains sensitive information, making privacy a major concern in deep learning. Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to conventional deep learning approaches. Nevertheless, SNNs still depend on large volumes of data, inheriting all the privacy challenges of deep learning. Homomorphic encryption addresses this challenge by allowing computations to be performed on encrypted data, ensuring data confidentiality throughout the entire processing pipeline. In this paper, we introduce PRIVSPIKE, a privacy-preserving inference framework for SNNs using the CKKS homomorphic encryption scheme. PRIVSPIKE supports arbitrary depth SNNs and introduces two key algorithms for evaluating the Leaky Integrate-and-Fire activation function: (1) a polynomial approximation algorithm designed for high-performance SNN inference, and (2) a novel scheme-switching algorithm that optimizes precision at a higher computational cost. We evaluate PRIVSPIKE on MNIST, CIFAR-10, Neuromorphic MNIST, and CIFAR-10 DVS using models from LeNet-5 and ResNet-19 architectures, achieving encrypted inference accuracies of 98.10%, 79.3%, 98.1%, and 66.0%, respectively. On a consumer-grade CPU, SNN LeNet-5 models achieved inference times of 28 seconds on MNIST and 212 seconds on Neuromorphic MNIST. For SNN ResNet-19 models, inference took 784 seconds on CIFAR-10 and 1846 seconds on CIFAR-10 DVS. These results establish PRIVSPIKE as a viable and efficient solution for secure SNN inference, bridging the gap between energy-efficient deep neural networks and strong cryptographic privacy guarantees while outperforming prior encrypted SNN solutions.
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