Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption
We propose a privacy-preserving graph neural network (GNN) inference framework tailored to this multi-bank setting.
ProofPaper ↗
Key points
- Anti-money laundering (AML) in financial networks requires modeling transaction patterns that span multiple institutions.
- 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.
- This formulation not only preserves the edge-attributed multigraph structure of transaction networks but also reduces every aggregation step to plaintext--ciphertext matrix products.
Sources (1)
- [1]Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic EncryptionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 09:43 AM
We propose a privacy-preserving graph neural network (GNN) inference framework tailored to this multi-bank setting.
Anti-money laundering (AML) in financial networks requires modeling transaction patterns that span multiple institutions.
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