English(EN) Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption

新的图神经网络框架支持隐私保护的金融欺诈检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一种新颖的隐私保护框架，用于金融交易图上的图神经网络（GNN）推理。该系统允许多家银行在不共享敏感账户和交易数据的情况下进行反洗钱分析。通过利用CKKS同态加密方案，该框架使得推理完全可以在加密特征上进行，确保明文数据不会暴露给中央服务器。该架构被设计为以边缘为中心，将复杂的聚合步骤简化为可以在加密下处理的矩阵乘积，并通过多项式函数近似非线性，以保持与CKKS的兼容性。 AI

影响在不泄露敏感交易数据的情况下，实现协作式金融欺诈检测。

排序理由详细介绍图神经网络新隐私保护推理框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · H. \c{C}a\u{g}r{\i} Bilgi, Kemal Mutluergil, Ceren Y{\i}ld{\i}r{\i}m, Kubilay Atasu, Erkay Savas · 2026-10-15 04:00

通过同态加密在金融交易图上进行隐私保护的GNN推理

arXiv:2610.18054v2 Announce Type: replace-cross Abstract: Anti-money laundering (AML) in financial networks requires modeling transaction patterns that span multiple institutions. In practice, however, banks cannot share account and transaction features due to strict privacy regu…

本文如何被排名

报道来源 [1]

通过同态加密在金融交易图上进行隐私保护的GNN推理

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