Privacy-Preserving Population-Enhanced Biometric Key Generation from Free-Text Keystroke Dynamics

TL;DR

This paper introduces a novel continuous biometric key generation method from free-text keystroke dynamics, utilizing privacy-preserving linear discriminant analysis to enhance security and reduce errors.

Contribution

It presents the first technique for continuous biometric key generation from free-text keystrokes, incorporating a privacy-preserving LDA protocol and a scaled parity code for improved accuracy.

Findings

Achieved around 5% equal error rate with LDA

Below 7% error rate without LDA

Demonstrated effectiveness on a dataset of 486 users

Abstract

Biometric key generation techniques are used to reliably generate cryptographic material from biometric signals. Existing constructions require users to perform a particular activity (e.g., type or say a password, or provide a handwritten signature), and are therefore not suitable for generating keys continuously. In this paper we present a new technique for biometric key generation from free-text keystroke dynamics. This is the first technique suitable for continuous key generation. Our approach is based on a scaled parity code for key generation (and subsequent key reconstruction), and can be augmented with the use of population data to improve security and reduce key reconstruction error. In particular, we rely on linear discriminant analysis (LDA) to obtain a better representation of discriminable biometric signals. To update the LDA matrix without disclosing user's biometric information, we design a provably secure privacy-preserving protocol (PP-LDA) based on homomorphic encryption. Our biometric key generation with PP-LDA was evaluated on a dataset of 486 users. We report equal error rate around 5% when using LDA, and below 7% without LDA.