论文标题
使用同态加密，保存隐私信用卡欺诈检测
Privacy-Preserving Credit Card Fraud Detection using Homomorphic Encryption
论文作者
论文摘要
信用卡欺诈是金融机构及其客户不断面临的问题，这是通过欺诈检测系统缓解的。但是，这些系统需要使用敏感的客户交易数据，这既引入了客户的隐私，又引入了对卡提供商的数据泄露脆弱性。本文提出了一个使用同态加密进行加密交易的私人欺诈检测系统。 XGBoost和一个Feelforward分类器神经网络的两种模型被训练为违反数据的欺诈探测器。然后将它们转换为使用同构加密进行私人推理的模型。讨论了延迟，存储和检测结果，以及用例以及部署的可行性。 XGBoost模型具有更好的性能，其加密推理低至6ms，而神经网络的加密推断为296ms。但是，神经网络实现仍然可能是优选的，因为它可以安全地部署更简单。还提供了该系统的代码库，以进行仿真和进一步开发。
Credit card fraud is a problem continuously faced by financial institutions and their customers, which is mitigated by fraud detection systems. However, these systems require the use of sensitive customer transaction data, which introduces both a lack of privacy for the customer and a data breach vulnerability to the card provider. This paper proposes a system for private fraud detection on encrypted transactions using homomorphic encryption. Two models, XGBoost and a feedforward classifier neural network, are trained as fraud detectors on plaintext data. They are then converted to models which use homomorphic encryption for private inference. Latency, storage, and detection results are discussed, along with use cases and feasibility of deployment. The XGBoost model has better performance, with an encrypted inference as low as 6ms, compared to 296ms for the neural network. However, the neural network implementation may still be preferred, as it is simpler to deploy securely. A codebase for the system is also provided, for simulation and further development.
