A Security Protection Scheme against Memory Side-Channel Attacks on NPU

doi:10.3969/j.issn.1671-1122.2025.06.012

Abstract

Abstract:

With rapid advancement of artificial intelligence technology, neural processing units(NPU) have been widely adopted in smartphones, autonomous vehicles, and edge computing. However, existing NPU architectures demonstrated vulnerabilities against memory side-channel attacks, where attackers could reverse-engineer deep neural networks(DNN) model structures and parameters by analyzing memory access patterns. To address this issue, this paper proposed NPUGuard, a security protection scheme featuring two core modules: feature map partitioning module and encrypted compression engine. The solution enhanced security through three approaches: layer boundary expansion, data address obfuscation, and data encryption protection. Experimental results show that NPUGuard effectively increases layer boundaries, expanding potential reverse-engineered network configurations from 24 to 7.86×105. The chaos mapping-based encryption algorithm achieves 60% storage reduction while encrypting sensitive data. Moreover, NPUGuard introduces only 5% performance overhead, demonstrating effective balance between security enhancement and computational efficiency.

Key words: side-channel protection, NPU security, feature map partitioner, data encryption and compression

CLC Number:

TP309

HU Wenao, YAN Fei, ZHANG Liqiang. A Security Protection Scheme against Memory Side-Channel Attacks on NPU[J]. Netinfo Security, 2025, 25(6): 977-987.

Figures/Tables 13

References 26

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混沌映射	映射函数	参数
Tent	${{x}_{i+1}}=\frac{{{x}_{i}}}{a}~,x\in \left( 0,a \right);{{x}_{i+1}}=\frac{{{x}_{i}}}{1-a},x\in \left[ a,1 \right)$	a = 0.49
Logisitc	${{x}_{i+1}}=a\times {{x}_{i}}\times \left( 1-{{x}_{i}} \right)$	a = 3.98
Sine	${{x}_{i+1}}=\frac{4}{a}\times \sin \left( \text{ }\!\!\pi\!\!\text{ }{{x}_{i}} \right)$	a = 4
Chebyshev	${{x}_{i+1}}=\cos \left( a\times {{\cos }^{-1}}{{x}_{i}} \right)$	a = 4
Cubic	${{x}_{i+1}}=a\times {{x}_{i}}\times \left( 1-x_{i}^{2} \right)$	a = 2.595