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New power side-channel attack infers ML training data from embedded device power traces

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel power side-channel membership inference attack (PSCMIA) that can determine if a specific data sample was used to train an embedded machine learning model. This method bypasses the need for traditional attacks that rely on model outputs like prediction probabilities or labels, instead analyzing power consumption traces during model execution. PSCMIA demonstrated effectiveness across various datasets, model architectures (FC and CNN), and embedded platforms, achieving high ROC-AUC values and outperforming label-only attacks in many configurations. AI

IMPACT This research highlights a new privacy risk for embedded ML systems, potentially requiring new defenses to protect training data.

RANK_REASON Academic paper detailing a new machine learning security vulnerability. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New power side-channel attack infers ML training data from embedded device power traces

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Academic paper detailing a new machine learning security vulnerability. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sahan Sanjaya, Prabhat Mishra ·

    Power Side-Channel Membership Inference Attack on Embedded Machine Learning

    arXiv:2610.10909v1 Announce Type: cross Abstract: Membership inference attacks (MIAs) threaten the privacy of machine learning (ML) training data by determining whether a sample was used to train a target model. Existing MIAs rely on model outputs, ranging from prediction probabi…

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