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EdgeRefine framework improves privacy-utility balance in Graph Neural Networks

By PulseAugur Editorial · [2 sources] ·

Researchers have developed EdgeRefine, a novel framework designed to enhance the privacy-utility balance in Graph Neural Networks (GNNs). This method addresses the challenge of sensitive link information leakage in graph data by employing adaptive edge refinement and Jaccard similarity for edge probability estimation. EdgeRefine aims to improve accuracy while maintaining strong privacy guarantees, outperforming existing methods by significant margins on benchmark datasets like ACM and Cora. AI

IMPACT Enhances the feasibility of using graph neural networks in privacy-sensitive applications by improving the trade-off between data utility and privacy.

RANK_REASON The cluster contains an academic paper detailing a new method for privacy-preserving graph learning.

Read on arXiv cs.LG →

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

EdgeRefine framework improves privacy-utility balance in Graph Neural Networks

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The cluster contains an academic paper detailing a new method for privacy-preserving graph learning.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Wenxiu Ding, Muzhi Liu, Zheng Yan, Mingjun Wang, Yifan Zhao, Qiao Liu ·

    EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy

    arXiv:2607.08659v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have shown considerable success in learning from graph-structured data, but their use in privacy-sensitive areas remains difficult because graph structure can leak sensitive link information. To satisfy …

  2. arXiv cs.LG TIER_1 English(EN) · Qiao Liu ·

    EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy

    Graph Neural Networks (GNNs) have shown considerable success in learning from graph-structured data, but their use in privacy-sensitive areas remains difficult because graph structure can leak sensitive link information. To satisfy edge-level differential privacy, a common approa…

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