Highly Reliable Key Generation From Electrocardiogram (ECG)

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.

[1]  Li Sun,et al.  ECG Analysis Using Multiple Instance Learning for Myocardial Infarction Detection , 2012, IEEE Transactions on Biomedical Engineering.

[2]  Carmen C. Y. Poon,et al.  A novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health , 2006, IEEE Communications Magazine.

[3]  Ralf Steinmetz,et al.  Handwriting: Feature Correlation Analysis for Biometric Hashes , 2004, EURASIP J. Adv. Signal Process..

[4]  Dimitrios Hatzinakos,et al.  Heart Biometrics: Theory, Methods and Applications , 2011 .

[5]  K.K. Venkatasubramanian,et al.  Plethysmogram-based secure inter-sensor communication in Body Area Networks , 2008, MILCOM 2008 - 2008 IEEE Military Communications Conference.

[6]  A Leier,et al.  Cryptography with DNA binary strands. , 2000, Bio Systems.

[7]  Dimitrios Hatzinakos,et al.  Medical biometrics in mobile health monitoring , 2011, Secur. Commun. Networks.

[8]  H. A. Garcia-Baleon,et al.  Cryptographic Key Generation from Biometric Data Using Wavelets , 2009, 2009 Electronics, Robotics and Automotive Mechanics Conference (CERMA).

[9]  Dimitrios Hatzinakos,et al.  Analysis of Human Electrocardiogram for Biometric Recognition , 2008, EURASIP J. Adv. Signal Process..

[10]  Hao Feng,et al.  Private key generation from on-line handwritten signatures , 2002, Inf. Manag. Comput. Secur..

[11]  Sabine Van Huffel,et al.  A Novel Algorithm for the Automatic Detection of Sleep Apnea From Single-Lead ECG , 2015, IEEE Transactions on Biomedical Engineering.

[12]  Norbert Henze,et al.  A class of invariant consistent tests for multivariate normality , 1990 .

[13]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Andrew Beng Jin Teoh,et al.  Dynamic Detection-Rate-Based Bit Allocation With Genuine Interval Concealment for Binary Biometric Representation , 2013, IEEE Transactions on Cybernetics.

[15]  Moncef Gabbouj,et al.  Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Biomedical Engineering.

[16]  D. Hatzinakos,et al.  ECG Biometric Recognition Without Fiducial Detection , 2006, 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference.

[17]  D. Mozaffarian,et al.  Heart disease and stroke statistics--2014 update: a report from the American Heart Association. , 2014, Circulation.

[18]  Sanjay Kumar Singh,et al.  Evaluation of Electrocardiogram for Biometric Authentication , 2012, J. Information Security.

[19]  Arun Ross,et al.  From the Iriscode to the Iris: A New Vulnerability of Iris Recognition Systems , 2012 .

[20]  Dimitrios Hatzinakos,et al.  HeartID: Cardiac biometric recognition , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[21]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[22]  C. Deisy,et al.  Efficient Dimensionality Reduction Approaches for Feature Selection , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[23]  R. Palaniappan,et al.  Data Encryption Using Event-related Brain Signals , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[24]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[25]  Yong Wang,et al.  ISAR Imaging of Rotating Target with Equal Changing Acceleration Based on the Cubic Phase Function , 2008, EURASIP J. Adv. Signal Process..

[26]  Joseph A. O'Sullivan,et al.  ECG Biometric Recognition: A Comparative Analysis , 2012, IEEE Transactions on Information Forensics and Security.

[27]  Berk Sunar,et al.  Differential template attacks on PUF enabled cryptographic devices , 2010, 2010 IEEE International Workshop on Information Forensics and Security.

[28]  J. Fierrez-Aguilar,et al.  Cryptographic key generation using handwritten signature , 2006, SPIE Defense + Commercial Sensing.

[29]  Zhe Jin,et al.  Biometric cryptosystems: A new biometric key binding and its implementation for fingerprint minutiae-based representation , 2016, Pattern Recognit..

[30]  Kang Ryoung Park,et al.  Biometric Key Binding: Fuzzy Vault Based on Iris Images , 2007, ICB.

[31]  I. Khalil,et al.  ECG biometric recognition in different physiological conditions using robust normalized QRS complexes , 2012, 2012 Computing in Cardiology.

[32]  Michael K. Reiter,et al.  Password hardening based on keystroke dynamics , 2002, International Journal of Information Security.

[33]  Chun Chen,et al.  Binary Biometric Representation through Pairwise Adaptive Phase Quantization , 2011, EURASIP J. Inf. Secur..

[34]  Hyun-Soo Choi,et al.  Biometric Authentication Using Noisy Electrocardiograms Acquired by Mobile Sensors , 2016, IEEE Access.

[35]  Guy Carrault,et al.  ECG biometric analysis in different physiological recording conditions , 2016, Signal Image Video Process..

[36]  Nalini K. Ratha,et al.  Generating Cancelable Fingerprint Templates , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Michael R. Chernick,et al.  Wavelet Methods for Time Series Analysis , 2001, Technometrics.

[38]  A. van Oosterom,et al.  Geometrical factors affecting the interindividual variability of the ECG and the VCG. , 2000, Journal of electrocardiology.

[39]  Raymond N. J. Veldhuis,et al.  Practical Biometric Authentication with Template Protection , 2005, AVBPA.

[40]  Robert Plonsey,et al.  Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields , 1995 .

[41]  Satoshi Hoshino,et al.  Impact of artificial "gummy" fingers on fingerprint systems , 2002, IS&T/SPIE Electronic Imaging.

[42]  Dimitrios Hatzinakos,et al.  Medical biometrics: The perils of ignoring time dependency , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[43]  David Chek Ling Ngo,et al.  Computation of Cryptographic Keys from Face Biometrics , 2003, Communications and Multimedia Security.

[44]  Chun Chen,et al.  Biometric Quantization through Detection Rate Optimized Bit Allocation , 2009, EURASIP J. Adv. Signal Process..

[45]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[46]  Carlo Sansone,et al.  Subject identification via ECG fiducial-based systems: Influence of the type of QT interval correction , 2015, Comput. Methods Programs Biomed..