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New method secures embedded neural networks against timing attacks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new methodology for implementing activation functions in embedded neural networks that prevents information leakage through timing side channels. This approach ensures consistent execution times across all inputs, regardless of the specific activation function used, by employing techniques like branchless selection and fixed-cost approximations. Tested on an ARM Cortex-M4 platform with common activation functions, the protected implementations achieved identical cycle counts while maintaining high numerical accuracy, offering a practical solution for secure embedded inference. AI

IMPACT Enhances security for embedded AI systems by mitigating timing-based side-channel attacks.

RANK_REASON The cluster contains an academic paper detailing a new methodology for implementing activation functions on microcontrollers. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method secures embedded neural networks against timing attacks

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The cluster contains an academic paper detailing a new methodology for implementing activation functions on microcontrollers. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, safety, infra
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High
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140 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiaolu Hou ·

    A Constant-Time Implementation Methodology for Activation Functions on Microcontrollers

    Embedded neural-network inference can leak information through timing side channels, including leakage caused by the evaluation of activation functions. This work proposes a constant-time implementation methodology for activation functions on embedded microcontrollers and validat…

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