A Secure, Flexible, and PPG-Based Biometric Scheme for Healthy IoT Using Homomorphic Random Forest

Abstract

Advances in the Internet of Things such as biosensing and camera-capturing technologies have also resulted in biometric-based authentication approaches becoming more viable. Photoplethysmography (PPG), for example, can be leveraged to provide better biometric features for continuous authentication, in comparison to other biometrics such as fingerprint. However, the fewer morphological features in PPG signals can complicate the accurate authentication of PPG signals. Furthermore, one has to also consider transmission security and PPG template storage security. These two considerations are typically not taken into consideration in most existing PPG-based authentication schemes. Therefore, we design a secure PPG-based biometric system to achieve accurate authentication with biometric privacy protection. In our design, Homomorphic Random Forest is adopted to classify the homomorphically encrypted biometric features, thus protecting the user’s PPG biometrics from being compromised in authentication. Furthermore, beat qualification screening is set up to avoid the interference of unqualified signals, and 19 features with the least redundancy from the 541 features extracted are selected as the biometric features of the user. Doing so allows us to ensure the accuracy of authentication, as demonstrated in our evaluations using five PPG databases collected by three different collection methods (contact, remote, and monitor). In addition, our experiments adopt PPG signals acquired from remote cameras, which are not considered in other PPG-based biometric systems. The experimental results show that the average accuracy of our biometric system is 96.4%, the F1 score is 96.1%, the EER is 2.14%, and the authentication time is about 0.5s.