用量子增强LSTM与隐私保护技术提升金融反欺诈精度。
A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection
- 将量子层嵌入LSTM，捕捉复杂交易模式
- 关键指标性能提升约5%，攻击防御效果优于传统方法
- 适合关注金融安全与隐私保护的研究者
数字交易快速增长带来欺诈活动激增，传统检测方法面临挑战。本文提出一种专用于金融反欺诈的联邦学习框架，首次将量子增强型长短期记忆（Quantum LSTM）模型与先进隐私保护技术结合。通过在LSTM架构中引入量子层，有效捕捉跨交易复杂模式，使关键评估指标性能提升约5%。框架核心为新型防御机制FedRansel，可抵御投毒与推断攻击，在模型退化与推理准确率方面较标准差分隐私降低4%-8%。该伪中心化设计在提升反欺诈准确率的同时，强化了敏感金融数据的安全性与机密性。
原文摘要 · Abstract (English)
Rapid growth of digital transactions has led to a surge in fraudulent activities, challenging traditional detection methods in the financial sector. To tackle this problem, we introduce a specialised federated learning framework that uniquely combines a quantum-enhanced Long Short-Term Memory (LSTM) model with advanced privacy preserving techniques. By integrating quantum layers into the LSTM architecture, our approach adeptly captures complex cross-transactional patters, resulting in an approximate 5% performance improvement across key evaluation metrics compared to conventional models. Central to our framework is "FedRansel", a novel method designed to defend against poisoning and inference attacks, thereby reducing model degradation and inference accuracy by 4-8%, compared to standard differential privacy mechanisms. This pseudo-centralised setup with a Quantum LSTM model, enhances fraud detection accuracy and reinforces the security and confidentiality of sensitive financial data.
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