联邦图学习框架保护隐私，防跨机构洗钱。
FedGraph-VASP: Privacy-Preserving Federated Graph Learning with Post-Quantum Security for Cross-Institutional Anti-Money Laundering
- 仅交换压缩后的账户嵌入，不暴露原始数据。
- 比特币数据集上F1达0.508，比现有方法高12.1%。
- 采用抗量子加密，适合金融反洗钱场景。
虚拟资产服务提供商在合规与用户隐私间面临矛盾，现有方法或需共享敏感交易数据，或孤立运行，难以发现跨链洗钱模式。我们提出FedGraph-VASP，一种隐私保护的联邦图学习框架，实现跨机构反洗钱协作而不泄露原始数据。核心是边界账户嵌入交换协议，仅传输压缩且不可逆的图神经网络表示，并使用后量子密码学（NIST标准的Kyber-512密钥封装+AES-256-GCM认证加密）保障安全。在椭圆比特币数据集上，采用真实Louvain划分，FedGraph-VASP达到F1分数0.508，优于生成基线FedSage+（F1=0.453），提升12.1%。低连接性下，生成插补性能下降，而本方法仍接近集中式表现（F1=0.620）。在以太坊欺诈检测数据集上，当跨域连接稀疏时，本方法表现较弱（F1=0.635），而FedSage+更优（F1=0.855），超越本地训练（F1=0.785）。结果揭示拓扑依赖权衡：嵌入交换适用于连接图，生成插补在高度模块化稀疏图中占优。隐私审计显示嵌入部分可逆（R²=0.32），限制特征精确恢复。
原文摘要 · Abstract (English)
Virtual Asset Service Providers (VASPs) face a fundamental tension between regulatory compliance and user privacy when detecting cross-institutional money laundering. Current approaches require either sharing sensitive transaction data or operating in isolation, leaving critical cross-chain laundering patterns undetected. We present FedGraph-VASP, a privacy-preserving federated graph learning framework that enables collaborative anti-money laundering (AML) without exposing raw user data. Our key contribution is a Boundary Embedding Exchange protocol that shares only compressed, non-invertible graph neural network representations of boundary accounts. These exchanges are secured using post-quantum cryptography, specifically the NIST-standardized Kyber-512 key encapsulation mechanism combined with AES-256-GCM authenticated encryption. Experiments on the Elliptic Bitcoin dataset with realistic Louvain partitioning show that FedGraph-VASP achieves an F1-score of 0.508, outperforming the state-of-the-art generative baseline FedSage+ (F1 = 0.453) by 12.1 percent on binary fraud detection. We further show robustness under low-connectivity settings where generative imputation degrades performance, while approaching centralized performance (F1 = 0.620) in high-connectivity regimes. We additionally evaluate generalization on an Ethereum fraud detection dataset, where FedGraph-VASP (F1 = 0.635) is less effective under sparse cross-silo connectivity, while FedSage+ excels (F1 = 0.855), outperforming even local training (F1 = 0.785). These results highlight a topology-dependent trade-off: embedding exchange benefits connected transaction graphs, whereas generative imputation can dominate in highly modular sparse graphs. A privacy audit shows embeddings are only partially invertible (R^2 = 0.32), limiting exact feature recovery.
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