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U.s.-u.k. Pets Prize Challenge: Anomaly Detection Via Privacy-enhanced Federated Learning.

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U.s.-u.k. Pets Prize Challenge: Anomaly Detection Via Privacy-enhanced Federated Learning.

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U.S.-U.K. PETs Prize Challenge: Anomaly Detection via Privacy-Enhanced Federated Learning.

Hafiz Asif¹, Sitao Min², Xinyue Wang²

¹Information Systems and Business Analytics Department, Hofstra University, Hempstead, NY 11549 USA.

IEEE Transactions on Privacy

|July 9, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Rutgers ScarletPets uses privacy-preserving federated learning for anomaly detection in financial transactions. This approach maintains high accuracy while protecting sensitive data, offering a flexible solution for financial crime prevention.

Keywords:

Anomaly detection differential privacy federated learning financial crime fraud detection payment network systems

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Area of Science:

Computer Science
Cybersecurity
Data Science

Background:

Collaborative analytics using large datasets is crucial in finance, healthcare, and national security.
Privacy Enhancing Technologies (PETs) are vital for enabling data collaboration without compromising privacy.
The U.S. and U.K. governments launched a PETs prize challenge in 2021 to address financial crime and pandemic response.

Purpose of the Study:

To present the Rutgers ScarletPets approach for privacy-preserving federated learning.
To identify anomalous financial transactions within a payment network system (PNS).
To develop a solution for the PETs prize challenge focused on financial crime prevention.

Main Methods:

A two-step anomaly detection methodology was employed.

Features were mined from account-level data and labels.

A privacy-preserving encoding scheme augmented features within the PNS.

The PNS learned a highly accurate classifier from the augmented data using federated learning.

Main Results:

The ScarletPets approach achieved no noteworthy drop in accuracy compared to centralized methods.
The federated learning model demonstrated high accuracy in identifying anomalous financial transactions.
The solution won first prize in the U.S. PETs prize challenge for its privacy, utility, efficiency, and flexibility.

Conclusions:

The Rutgers ScarletPets approach offers a flexible and accurate method for privacy-preserving anomaly detection in financial networks.
This federated learning model enables continuous improvement without additional computational or privacy burdens on participating banks.
The approach effectively addresses the need for secure collaborative analytics in sensitive domains like finance.