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Collaborative Fraud Detection on Large Scale Graph Using Secure Multi-Party Computation

Published: 21 October 2024 Publication History

Abstract

Enabling various parties to share data enhances online fraud detection capabilities considering fraudsters tend to reuse resources attacking multiple platforms. Multi-party computation (MPC) techniques, such as secret sharing, offer potential privacy-preserving solutions but face efficiency challenges when handling large-scale data. This paper presents a novel approach, SecureFD (Secure Fraud Detector), aimed at detecting fraud in multi-party graph data, ensuring privacy, accuracy, and scalability. We propose a graph neural network EPR-GNN, which is MPC-friendly, as the base detector. Then we design a framework that allows multiple parties to train EPR-GNN collaboratively on secure sparse graphs in a privacy- preserving manner. The oblivious node embedding sharing protocol in the collaborative training procedure achieves up to a 45× speed-up, supporting over four million users compared to the naive solution. Additionally, we further reduce secure computation by locally pruning a significant number of non-suspicious users and selecting only the most valuable resources for sharing. Experiments on real datasets demonstrate that by securely integrating data from different parties, SecureFD achieves superior detection performance compared to state-of-the-art local detectors. And the local pruning greatly improves the scalability without compromising detection accuracies.

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    cover image ACM Conferences
    CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
    October 2024
    5705 pages
    ISBN:9798400704369
    DOI:10.1145/3627673
    This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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    Published: 21 October 2024

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    Author Tags

    1. fraud detection
    2. graph neural network
    3. privacy-preserving
    4. secure multi-party computation

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