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New GNN Framework Enables Privacy-Preserving Financial Fraud Detection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel privacy-preserving framework for graph neural network (GNN) inference on financial transaction graphs. This system allows multiple banks to perform anti-money laundering analysis without sharing sensitive account and transaction data. By utilizing the CKKS homomorphic encryption scheme, the framework enables inference to be conducted entirely on encrypted features, ensuring that no plaintext data is exposed to a central server. The architecture is designed to be edge-centric, reducing complex aggregation steps to matrix products that can be processed under encryption, and approximating non-linearities with polynomial functions to maintain compatibility with CKKS. AI

IMPACT Enables collaborative financial fraud detection without compromising sensitive transaction data.

RANK_REASON Academic paper detailing a new privacy-preserving inference framework for GNNs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New GNN Framework Enables Privacy-Preserving Financial Fraud Detection

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Academic paper detailing a new privacy-preserving inference framework for GNNs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  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 ·

    Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption

    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…

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