Secure biometric systems based on bio-signals and DNA encryption of optical spectrograms

Abstract

Recently, biometrics has become widely used in applications to verify an individual's identity. To address security issues, biometrics presents an intriguing window of opportunity to enhance the usability and security of the Internet of Things (IoT) and other systems. It can be used to secure a variety of newly emerging IoT devices. However, biometric scenarios need more protection against different hacking attempts. Various solutions are introduced to secure biometrics. Cryptosystems, cancelable biometrics, and hybrid systems are efficient solutions for template protection. The new trend in biometric authentication systems is to use bio-signals. In this paper, two proposed authentication systems are introduced based on bio-signals. One of them is unimodal, while the other is multimodal. Protected templates are obtained depending on encryption. The deoxyribonucleic acid (DNA) encryption is implemented on the obtained optical spectrograms of bio-signals. The authentication process relies on the DNA sensitivity to variations in the initial values. In the multimodal system, the singular value decomposition (SVD) algorithm is implemented to merge bio-signals. Different evaluation metrics are used to assess the performance of the proposed systems. Simulation results prove the high accuracy and efficiency of the proposed systems as the equal error rate (EER) value is close to 0 and the area under the receiver operator characteristic curve (AROC) is close to 1. The false accept rate (FAR), false reject rate (FRR), and decidability (D) are also estimated with acceptable results of 1.6 × 10−8, 9.05 × 10−6, and 29.34, respectively. Simulation results indicate the performance stability of the proposed systems in the presence of different levels of noise.

A double encryption protection algorithm for stem cell bank privacy data based on improved AES and chaotic encryption technology

“…The DNA molecule consists mainly of four types of nucleotides, which are A(adenine), G(guanine), C(cytosine), T(thymine), wherein A and T, G and C are complementary pairs [40]. Data in the computer is stored in binary form.…”