Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection

2024/08/03 by Md. Saikat Islam Khan, Aparna Gupta, Khan, Md. Saikat Islam +5
Computer Science · Social Sciences · #Imbalanced Data Classification Techniques #Crime, Illicit Activities, and Governance #Blockchain Technology Applications and Security

paper · pdf · doi:10.48550/arxiv.2408.01609

Abstract

We introduce Federated Learning for Relational Data (Fed-RD), a novel privacy-preserving federated learning algorithm specifically developed for financial transaction datasets partitioned vertically and horizontally across parties. Fed-RD strategically employs differential privacy and secure multiparty computation to guarantee the privacy of training data. We provide theoretical analysis of the end-to-end privacy of the training algorithm and present experimental results on realistic synthetic datasets. Our results demonstrate that Fed-RD achieves high model accuracy with minimal degradation as privacy increases, while consistently surpassing benchmark results.

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