Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption
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 regulations. We propose a privacy-preserving graph neural network (GNN) inference framework tailored to this multi-bank setting. A central server holds the graph connectivity and pretrained model weights in plaintext, while each bank keeps its transaction features encrypted. Inference is then carried out entirely on encrypted features under the CKKS homomorphic encryption scheme, so that no plaintext transaction features or predictions are exposed to the server. The key to m...
| Authors | H. Çağrı Bilgi, Kemal Mutluergil, Ceren Yıldırım et al. |
|---|---|
| Primary category | cs.CR |
| Published | 13 October 2026 |
| Last updated at source | 14 October 2026 |
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