Highly Reliable Key Generation From Electrocardiogram (ECG)

• Published in: IEEE transactions on biomedical engineering Vol. 64; no. 6; pp. 1400 - 1411
• Main Authors: Karimian, Nima, Guo, Zimu, Tehranipoor, Mark, Forte, Domenic
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-06-2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; Algorithms; Authentication; Biometric Identification - methods; Biometrics; Biometrics (access control); Computer Security; Computer Simulation; Cryptography; Discrete cosine transform; EKG; electrocardiogram (ECG); Electrocardiography; Electrocardiography - methods; Entropy; Feature extraction; Heart; Heart Rate - physiology; Humans; Immunity; key generation; Mapping; min-entropy; Models, Statistical; Pattern Recognition, Automated - methods; Public Key Infrastructure; quantization; Reliability; wavelet; Wavelet analysis
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2016.2607020

Traditional passwords are inadequate as cryptographic keys, as they are easy to forge and are vulnerable to guessing. Human biometrics have been proposed as a promising alternative due to their intrinsic nature. Electrocardiogram (ECG) is an emerging biometric that is extremely difficult to forge and circumvent, but has not yet been heavily investigated for cryptographic key generation. ECG has challenges with respect to immunity to noise, abnormalities, etc. In this paper, we propose a novel key generation approach that extracts keys from real-valued ECG features with high reliability and entropy in mind. Our technique, called interval optimized mapping bit allocation (IOMBA), is applied to normal and abnormal ECG signals under multiple session conditions. We also investigate IOMBA in the context of different feature extraction methods, such as wavelet, discrete cosine transform, etc., to find the best method for feature extraction. Experiments of IOMBA show that 217-, 38-, and 100-bit keys with 99.9%, 97.4%, and 95% average reliability and high entropy can be extracted from normal, abnormal, and multiple session ECG signals, respectively. By allowing more errors or lowering entropy, key lengths can be further increased by tunable parameters of IOMBA, which can be useful in other applications. While IOMBA is demonstrated on ECG, it should be useful for other biometrics as well.