A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Farzad Nikfam, Raffaele Casaburi, Alberto Marchisio, Maurizio Martina, Muhammad Shafique
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引用次数: 0

Abstract

Machine learning (ML) is widely used today, especially through deep neural networks (DNNs); however, increasing computational load and resource requirements have led to cloud-based solutions. To address this problem, a new generation of networks has emerged called spiking neural networks (SNNs), which mimic the behavior of the human brain to improve efficiency and reduce energy consumption. These networks often process large amounts of sensitive information, such as confidential data, and thus privacy issues arise. Homomorphic encryption (HE) offers a solution, allowing calculations to be performed on encrypted data without decrypting them. This research compares traditional DNNs and SNNs using the Brakerski/Fan-Vercauteren (BFV) encryption scheme. The LeNet-5 and AlexNet models, widely-used convolutional architectures, are used for both DNN and SNN models based on their respective architectures, and the networks are trained and compared using the FashionMNIST dataset. The results show that SNNs using HE achieve up to 40% higher accuracy than DNNs for low values of the plaintext modulus t, although their execution time is longer due to their time-coding nature with multiple time steps.
一种保护隐私的脉冲神经网络的同态加密框架
机器学习(ML)今天被广泛使用,特别是通过深度神经网络(dnn);然而,不断增加的计算负载和资源需求导致了基于云的解决方案。为了解决这个问题,新一代的神经网络出现了,称为峰值神经网络(snn),它模仿人类大脑的行为来提高效率和减少能量消耗。这些网络通常处理大量敏感信息,例如机密数据,因此出现隐私问题。同态加密(HE)提供了一种解决方案,允许在不解密加密数据的情况下对其执行计算。本研究比较了使用Brakerski/Fan-Vercauteren (BFV)加密方案的传统dnn和snn。广泛使用的卷积架构LeNet-5和AlexNet模型基于各自的架构用于DNN和SNN模型,并使用FashionMNIST数据集对网络进行训练和比较。结果表明,在较低的明文模数t值下,使用HE的snn的准确率比dnn高40%,尽管它们的执行时间更长,因为它们具有多个时间步长的时间编码性质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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