2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection
- URL: http://arxiv.org/abs/2310.08335v1
- Date: Thu, 12 Oct 2023 13:48:26 GMT
- Title: 2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection
- Authors: Zhirui Pan, Guangzhong Wang, Zhaoning Li, Lifeng Chen, Yang Bian,
Zhongyuan Lai
- Abstract summary: We propose a novel two-stage approach to federated graph learning (2SFGL)
2SFGL involves the virtual fusion of multiparty graphs, and the second involves model training and inference on the virtual graph.
We evaluate our framework on a conventional fraud detection task based on the FraudAmazonDataset and FraudYelpDataset.
- Score: 1.6427658855248812
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Financial crime detection using graph learning improves financial safety and
efficiency. However, criminals may commit financial crimes across different
institutions to avoid detection, which increases the difficulty of detection
for financial institutions which use local data for graph learning. As most
financial institutions are subject to strict regulations in regards to data
privacy protection, the training data is often isolated and conventional
learning technology cannot handle the problem. Federated learning (FL) allows
multiple institutions to train a model without revealing their datasets to each
other, hence ensuring data privacy protection. In this paper, we proposes a
novel two-stage approach to federated graph learning (2SFGL): The first stage
of 2SFGL involves the virtual fusion of multiparty graphs, and the second
involves model training and inference on the virtual graph. We evaluate our
framework on a conventional fraud detection task based on the
FraudAmazonDataset and FraudYelpDataset. Experimental results show that
integrating and applying a GCN (Graph Convolutional Network) with our 2SFGL
framework to the same task results in a 17.6\%-30.2\% increase in performance
on several typical metrics compared to the case only using FedAvg, while
integrating GraphSAGE with 2SFGL results in a 6\%-16.2\% increase in
performance compared to the case only using FedAvg. We conclude that our
proposed framework is a robust and simple protocol which can be simply
integrated to pre-existing graph-based fraud detection methods.
Related papers
- Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering [4.1964397179107085]
This research presents a novel privacy-preserving approach for collaborative AML machine learning.
It facilitates secure data sharing across institutions and borders while preserving privacy and regulatory compliance.
The research contributes two key privacy-preserving pipelines.
arXiv Detail & Related papers (2024-11-05T09:13:53Z) - Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks [50.19590901147213]
Graph neural networks (GNNs) have become instrumental in diverse real-world applications, offering powerful graph learning capabilities.
GNNs are vulnerable to adversarial attacks, including membership inference attacks (MIA)
This paper proposes an effective two-stage defense, Graph Transductive Defense (GTD), tailored to graph transductive learning characteristics.
arXiv Detail & Related papers (2024-06-12T06:36:37Z) - DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities [12.460745260973837]
Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry.
Recent advances in deep learning, especially Graph Neural Networks (GNN), have uncovered the feasibility of automatic detection of a wide range of software vulnerabilities.
In this work, we are one of the first to explore heterogeneous graph representation in the form of Code Property Graph.
arXiv Detail & Related papers (2023-06-02T08:57:13Z) - Learning Strong Graph Neural Networks with Weak Information [64.64996100343602]
We develop a principled approach to the problem of graph learning with weak information (GLWI)
We propose D$2$PT, a dual-channel GNN framework that performs long-range information propagation on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.
arXiv Detail & Related papers (2023-05-29T04:51:09Z) - Model Inversion Attacks against Graph Neural Networks [65.35955643325038]
We study model inversion attacks against Graph Neural Networks (GNNs)
In this paper, we present GraphMI to infer the private training graph data.
Our experimental results show that such defenses are not sufficiently effective and call for more advanced defenses against privacy attacks.
arXiv Detail & Related papers (2022-09-16T09:13:43Z) - Certified Graph Unlearning [39.29148804411811]
Graph-structured data is ubiquitous in practice and often processed using graph neural networks (GNNs)
We introduce the first known framework for emph certified graph unlearning of GNNs.
Three different types of unlearning requests need to be considered, including node feature, edge and node unlearning.
arXiv Detail & Related papers (2022-06-18T07:41:10Z) - FedGraph: Federated Graph Learning with Intelligent Sampling [7.798227884125872]
Federated learning has attracted much research attention due to its privacy protection in distributed machine learning.
Existing work of federated learning mainly focuses on Convolutional Neural Network (CNN), which cannot efficiently handle graph data that are popular in many applications.
In this paper, we propose FedGraph for federated graph learning among multiple computing clients, each of which holds a subgraph.
arXiv Detail & Related papers (2021-11-02T04:58:03Z) - Deep Fraud Detection on Non-attributed Graph [61.636677596161235]
Graph Neural Networks (GNNs) have shown solid performance on fraud detection.
labeled data is scarce in large-scale industrial problems, especially for fraud detection.
We propose a novel graph pre-training strategy to leverage more unlabeled data.
arXiv Detail & Related papers (2021-10-04T03:42:09Z) - GraphMI: Extracting Private Graph Data from Graph Neural Networks [59.05178231559796]
We present textbfGraph textbfModel textbfInversion attack (GraphMI), which aims to extract private graph data of the training graph by inverting GNN.
Specifically, we propose a projected gradient module to tackle the discreteness of graph edges while preserving the sparsity and smoothness of graph features.
We design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
arXiv Detail & Related papers (2021-06-05T07:07:52Z) - GraphFL: A Federated Learning Framework for Semi-Supervised Node
Classification on Graphs [48.13100386338979]
We propose the first FL framework, namely GraphFL, for semi-supervised node classification on graphs.
We propose two GraphFL methods to respectively address the non-IID issue in graph data and handle the tasks with new label domains.
We adopt representative graph neural networks as GraphSSC methods and evaluate GraphFL on multiple graph datasets.
arXiv Detail & Related papers (2020-12-08T03:13:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.