Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection

• URL: http://arxiv.org/abs/2401.10765v2
• Date: Mon, 22 Jan 2024 08:17:42 GMT
• Title: Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection
• Authors: Aydin Abadi, Bradley Doyle, Francesco Gini, Kieron Guinamard, Sasi Kumar Murakonda, Jack Liddell, Paul Mellor, Steven J. Murdoch, Mohammad Naseri, Hector Page, George Theodorakopoulos, Suzanne Weller
• Abstract summary: Federated Learning (FL) is a data-minimization approach enabling collaborative model training across diverse clients with local data. State-of-the-art FL solutions to identify fraudulent financial transactions exhibit a subset of the following limitations. We introduce Starlit, a novel scalable privacy-preserving FL mechanism that overcomes these limitations.
• Score: 2.436659710491562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) is a data-minimization approach enabling collaborative model training across diverse clients with local data, avoiding direct data exchange. However, state-of-the-art FL solutions to identify fraudulent financial transactions exhibit a subset of the following limitations. They (1) lack a formal security definition and proof, (2) assume prior freezing of suspicious customers' accounts by financial institutions (limiting the solutions' adoption), (3) scale poorly, involving either $O(n^2)$ computationally expensive modular exponentiation (where $n$ is the total number of financial institutions) or highly inefficient fully homomorphic encryption, (4) assume the parties have already completed the identity alignment phase, hence excluding it from the implementation, performance evaluation, and security analysis, and (5) struggle to resist clients' dropouts. This work introduces Starlit, a novel scalable privacy-preserving FL mechanism that overcomes these limitations. It has various applications, such as enhancing financial fraud detection, mitigating terrorism, and enhancing digital health. We implemented Starlit and conducted a thorough performance analysis using synthetic data from a key player in global financial transactions. The evaluation indicates Starlit's scalability, efficiency, and accuracy.

Related papers

• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• 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)
• Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs [49.57641083688934]
  We introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines.
  arXiv  Detail & Related papers  (2024-06-05T20:19:09Z)
• Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection [0.9831489366502302]
  This research introduces a novel approach using Federated Learning (FL) and Explainable AI (XAI) to address these challenges. FL enables financial institutions to collaboratively train a model to detect fraudulent transactions without directly sharing customer data. XAI ensures that the predictions made by the model can be understood and interpreted by human experts, adding a layer of transparency and trust to the system.
  arXiv  Detail & Related papers  (2023-12-20T18:26:59Z)
• Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs [30.260427020479536]
  In this paper, we propose a novel and highly efficient solution RiseFL for secure and verifiable data collaboration. Firstly, we devise a probabilistic integrity check method that significantly reduces the cost of ZKP generation and verification. Thirdly, we design a hybrid commitment scheme to satisfy Byzantine robustness with improved performance.
  arXiv  Detail & Related papers  (2023-11-26T14:19:46Z)
• Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification [51.04894019092156]
  Federated learning (FL) has been recognized as a rapidly growing area, where the model is trained over clients under the FL orchestration (PS) In this paper, we propose a novel primal sparification algorithm for and guarantee non-smooth FL problems. Its unique insightful properties and its analyses are also presented.
  arXiv  Detail & Related papers  (2023-10-30T14:15:47Z)
• Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection [11.167661320589488]
  In real-world financial anomaly detection scenarios, the data is partitioned both vertically and horizontally. Our solution combines fully homomorphic encryption (HE), secure multi-party computation (SMPC), differential privacy (DP) Our solution won second prize in the first phase of the U.S. Privacy Enhancing Technologies (PETs) Prize Challenge.
  arXiv  Detail & Related papers  (2023-10-30T06:51:33Z)
• Privacy-Preserving Financial Anomaly Detection via Federated Learning & Multi-Party Computation [17.314619091307343]
  We describe a privacy-preserving framework that allows financial institutions to jointly train highly accurate anomaly detection models. We show that our solution enables the network to train a highly accurate anomaly detection model while preserving privacy of customer data.
  arXiv  Detail & Related papers  (2023-10-06T19:16:41Z)
• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• Dynamic Attention-based Communication-Efficient Federated Learning [85.18941440826309]
  Federated learning (FL) offers a solution to train a global machine learning model. FL suffers performance degradation when client data distribution is non-IID. We propose a new adaptive training algorithm $textttAdaFL$ to combat this degradation.
  arXiv  Detail & Related papers  (2021-08-12T14:18:05Z)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients [124.48732110742623]
  We propose a novel framework by integrating blockchain into Federated Learning (FL) BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning. It gives rise to a new problem of training deficiency, caused by lazy clients who plagiarize others' trained models and add artificial noises to conceal their cheating behaviors.
  arXiv  Detail & Related papers  (2020-12-02T12:18:27Z)

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.