Cancelable electrocardiogram biometric system based on chaotic encryption using three‐dimensional logistic map for biometric‐based cloud services

Abstract

Biometric features are extensively employed for security purposes, but they are vulnerable to threats and can be lost or compromised. Electrocardiogram (ECG) has been utilized as one of the most favorable biometrics. However, it contains confidential patient health information and personal identification details. Moreover, security flaws take place in hacking scenarios. Therefore, original biometrics must be secured by preventing them from being used in biometric databases. The enhanced security trend in biometric authentication is cancelable biometrics. Cancelable biometric systems can be built by generating regularly repeated distortions of biometric features to secure the sensitive data of the users. When cancelable features are disclosed, distortion parameters are modified, and new cancelable templates are generated. This paper presents a proposed cancelable ECG recognition system based on the 3D chaotic logistic map encryption, which has highly efficient random characteristics with confusion and diffusion properties. Normal and abnormal ECG signals have been used to test the proposed cancelable biometric recognition system, and good access results have been obtained. The results of the simulation indicate that the proposed system is secure, reliable, and practicable even with ECG signals for patients.

Feature extraction and learning approaches for cancellable biometrics: A survey

“…In this work, the R-peak detection is carried out using methods like the Pan-Tompkins algorithm, thus allowing the extraction of R-peak-aligned ECG pulses to be feature data. Eldesouky et al [82] transformed ECG signals into spectrograms, whose pixel values constitute the ECG feature data. The authors built a cancellable ECG recognition system using the 3D chaotic logic map.…”

“…Eldesouky et al. [82] transformed ECG signals into spectrograms, whose pixel values constitute the ECG feature data. The authors built a cancellable ECG recognition system using the 3D chaotic logic map.…”