跨境金融数据共享中保护隐私，实现多方联合建模。
Graph Privacy: A Heterogeneous Federated GNN for Trans-Border Financial Data Circulation
- 用联邦图神经网络将异构数据构建成全局图进行安全协作。
- 实验显示准确率更高且收敛更快，优于现有方法。
- 适合需要跨机构数据合作又严守隐私的金融场景。
金融机构间外部数据共享需求强烈，但隐私问题导致平台难以互联、数据开放度低。为解决跨境金融数据流动与共享中的隐私问题，确保数据可用不可见，并实现不同行业商业组织的异构数据联合画像，我们提出一种异构联邦图神经网络（HFGNN）方法。该方法将跨境组织的异构业务数据分布视为子图，通过中心服务器构建统计异构的全局图，实现子图间的数据流通。每个子图通过本地训练学习个性化服务模型，选择并更新聚合参数相关的子图子集，有效分离与融合子图间的拓扑与特征信息。模拟实验结果表明，所提方法在准确率和收敛速度上均优于现有方法。
原文摘要 · Abstract (English)
The sharing of external data has become a strong demand of financial institutions, but the privacy issue has led to the difficulty of interconnecting different platforms and the low degree of data openness. To effectively solve the privacy problem of financial data in trans-border flow and sharing, to ensure that the data is available but not visible, to realize the joint portrait of all kinds of heterogeneous data of business organizations in different industries, we propose a Heterogeneous Federated Graph Neural Network (HFGNN) approach. In this method, the distribution of heterogeneous business data of trans-border organizations is taken as subgraphs, and the sharing and circulation process among subgraphs is constructed as a statistically heterogeneous global graph through a central server. Each subgraph learns the corresponding personalized service model through local training to select and update the relevant subset of subgraphs with aggregated parameters, and effectively separates and combines topological and feature information among subgraphs. Finally, our simulation experimental results show that the proposed method has higher accuracy performance and faster convergence speed than existing methods.
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