  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.
