الفهرس | Only 14 pages are availabe for public view |
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. Due to uniqueness of electrocardiogram (ECG) is very high even in identical twins, so it can be used as biometric signature. However, it contains confidential patient health information and personal identification details. ECG needs to be encrypted before transmission through public network to avoid the data being breached and hacked. On the other hand, caregivers or doctors receive the encrypted ECG signal, which can be decrypted using same key and analyze of patient’s ECG signal. 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. In this thesis, encryption technique based on convolution and substitution is proposed. By taking random values of ECG signal and multiplying by random number, then get an average of them to be encrypted. Finally, chaotic ECG signal is completely different than original signal. The security of the proposed system will depend on random kernel coefficients, substitution process and length of kernel filter. Simulation results show that the proposed system is capable of encrypting ECG signals for secure communication efficiently. Cancelable ECG recognition system based on the 3D chaotic logistic map encryption is also proposed, 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. For all the proposed systems, the experimental results on different datasets prove that the proposed systems achieve high ability to efficiently encrypt different biometric databases of normal ECG signals or abnormal ones that provide better recognition performance with high efficiency. These results show that the proposed systems are secure, reliable, feasible and also achieve high correlation rates with low equal error rates (EER) that proved high security levels of the systems. |