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New TGHE framework enables privacy-preserving GNN inference on large graphs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed TGHE, a novel framework for privacy-preserving Graph Neural Network (GNN) inference in edge-cloud systems. Unlike previous graph-centric approaches that struggle with large datasets, TGHE utilizes an ego-centric method by exploiting a template phenomenon in transaction graphs. This allows it to canonicalize and pack structurally similar local computation trees into shared ciphertexts for parallel processing, significantly improving efficiency and enabling analysis of much larger graphs. AI

IMPACT This framework could enable privacy-preserving analysis of large-scale dynamic graphs in fields like finance.

RANK_REASON The cluster contains an academic paper detailing a new technical framework for GNNs. [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 TGHE framework enables privacy-preserving GNN inference on large graphs

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The cluster contains an academic paper detailing a new technical framework for GNNs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ngoc Bao Anh Le, Thai T. Vu, John Le, Heath Cooper, Jun Shen ·

    TGHE: Template-based Graph Homomorphic Encryption for Privacy-Preserving GNN Inference in Edge-Cloud Systems

    arXiv:2606.26664v1 Announce Type: cross Abstract: Existing homomorphic encryption (HE)-based GNN systems adopt a graph-centric paradigm that couples per-query cost to global graph size, limiting evaluations to at most ~20k nodes and making them incompatible with dynamic, large-sc…

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