English(EN) GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models

新的GNNBleed攻击揭示私有图数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-26 04:00

研究人员开发了一种名为GNNBleed的新方法，用于推理图神经网络（GNN）中的私有边。该攻击即使在对GNN模型的黑盒访问有限的情况下也有效，并且在图结构随时间变化的动态图上也很有效。GNNBleed在静态和动态场景下均取得了高F1分数，显著优于现有方法。 AI

影响这项研究突显了GNN中潜在的隐私漏洞，有必要开发更强大的图数据隐私保护技术。

排序理由该项目是一篇研究论文，详细介绍了一种针对图神经网络的新攻击方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Zeyu Song, Ehsanul Kabir, Shagufta Mehnaz · 2026-08-26 04:00

GNNBleed：揭示具有GNN模型现实访问的图中私有边的推理攻击

arXiv:2311.16139v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have become indispensable tools for learning from graph structured data, catering to various applications such as social network analysis and fraud detection for financial services. At the hear…