Privacy-Preserving Credit Card Fraud Detection using Homomorphic Encryption
This addresses privacy concerns for customers and data breach vulnerabilities for financial institutions, though it is incremental as it applies existing encryption methods to fraud detection.
The paper tackles the problem of privacy in credit card fraud detection by proposing a system that uses homomorphic encryption to perform private inference on encrypted transactions, achieving encrypted inference times as low as 6ms for an XGBoost model and 296ms for a neural network.
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.