Key result
The NA-IOMBA framework outperformed standard IOMBA across multiple biometric modalities, achieving an equal error rate of 1.25% and 98.76% accuracy for ECG-based authentication.
Why the study?
Biometric cryptosystem processing and template protection costs are nontrivial for low-power, pervasive IoT devices, motivating frameworks to improve cost versus utility.
The NA-IOMBA framework using ECG biometrics offers an efficient and reliable method for access control and data protection in resource-constrained IoT devices.
May enhance ECG biometrics for IoT security; leaves open clinical validation before adoption.
With the emergence of the Internet-of-Things, there is a growing need for access control and data protection on low-power, pervasive devices. Key-based biometric cryptosystems are promising for IoT due to its convenient nature and lower susceptibility to attacks. However, the costs associated with biometric processing and template protection are nontrivial for smart cards, and so forth. In this paper, we discuss the cost versus the utility of biometric systems and investigate frameworks for improving them. We propose the noise-aware biometric quantization framework (NA-IOMBA) capable of generating unique, reliable, and high entropy keys with low enrollment times and costs with several experiments. First, we compare its performance with IOMBA and one-class-SVM on multiple biometric modalities, including popular ones (fingerprint and iris) and emerging cardiovascular ones (ECG and PPG). The results show that NA-IOMBA outperforms them all and that ECG provides the best trade-off between reliability, key length, entropy, and implementation cost. Second, we examine the impact on key reliability with ECGs obtained at different sessions and trained with a different number of heartbeats. Finally, implementation results show that incorporating noise models with NA-IOMBA reduces power and utilization overhead by more than 60% by adapting the pre-processing, feature extraction, and post-processing modules.
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Karimian et al. (2019) studied this question. Noise-aware interval optimized mapping bit allocation (NA-IOMBA) vs. IOMBA and one-class-SVM was evaluated on Equal Error Rate (EER) for ECG authentication. The NA-IOMBA framework outperformed standard IOMBA across multiple biometric modalities, achieving an equal error rate of 1.25% and 98.76% accuracy for ECG-based authentication.
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