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Gradient leakage attacks threaten GNNs in circuit design

By PulseAugur Editorial · [2 sources] ·

A new research paper details the first comprehensive evaluation of gradient leakage attacks (GLAs) on graph neural networks (GNNs) used in circuit design and hardware security. The study reveals that GLAs can expose sensitive information like gate types and hardware Trojan properties, potentially aiding adversaries. While some GNN architectures like GIN offer more resilience, others like GAT can exacerbate leakage. Existing defense techniques show limited effectiveness and can degrade model performance, indicating a need for more robust privacy-preserving solutions. AI

IMPACT Highlights potential security vulnerabilities in AI models used for critical infrastructure, necessitating development of more robust privacy-preserving techniques.

RANK_REASON The cluster contains a research paper detailing a new security vulnerability and evaluation of existing defenses for graph neural networks.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Gradient leakage attacks threaten GNNs in circuit design

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The cluster contains a research paper detailing a new security vulnerability and evaluation of existing defenses for graph neural networks.
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94 days old
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Rupesh Raj Karn, Johann Knechtel, Ozgur Sinanoglu ·

    Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks

    arXiv:2606.25589v1 Announce Type: new Abstract: As graph neural networks (GNNs) become standard tools for critical tasks in circuit design and analysis, their security and privacy risks require careful attention. Here, we present the first comprehensive evaluation of gradient lea…

  2. arXiv cs.LG TIER_1 English(EN) · Ozgur Sinanoglu ·

    Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks

    As graph neural networks (GNNs) become standard tools for critical tasks in circuit design and analysis, their security and privacy risks require careful attention. Here, we present the first comprehensive evaluation of gradient leakage attacks (GLAs) on GNNs in circuit-design an…

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