Privacy-Preserving Keystroke Analysis using Fully Homomorphic Encryption & Differential Privacy

Keystroke dynamics is a behavioural biometric form of authentication based on the inherent typing behaviour of an individual. While this technique is gaining traction, protecting the privacy of the users is of the utmost importance. Fully Homomorphic Encryption is a technique that allows performing computation on encrypted data, which enables processing of sensitive data. FHE is also known to be "future-proof" since it is a lattice-based cryptosystem that is regarded as quantum-safe. It has seen significant performance improvements over the years with substantially increased developer-friendly tools. We propose a neural network for keystroke analysis trained using differential privacy to speed up training while preserving privacy and predicting on encrypted data using FHE to keep the users' privacy intact while having sufficient usability.