Abstract
For intelligent medical systems, clinical information security is one of critical requirements. Recently, random binary sequences (RBSs) based on interpulse intervals (IPIs) were applied as secret keys to secure medical information in medical applications. Most of the existing RBS generation methods acquire a uniform quantity of bits per IPI, thereby requiring more processing time. However, the functional capacity of humans influences their heart rate variability, thereby resulting in the extraction of adaptive entropic bits per IPI across individuals. Therefore, adaptive computing-based RBS generation method is proposed to extract a variable number of bits on the basis of the heart rate (HR) bands of individuals to provide a balance between processing time and security in WBSNs. The proposed method is evaluated by using ECG recordings of 126 subjects with dynamic scenarios. Our experimental results that 128-bit RBSs generated by applying the proposed method can be used as secret keys for entity identifiers or patient’s data encryption for securing intelligent medical applications. In this study, the hamming distance metric is used to measure the uniqueness of the generated RBSs, and randomness of RBSs is computed by means of statistical tests, for different HR bands. Furthermore, the processing time of the proposed method for RBS generation shows improvement compared with the conventional techniques. The proposed approach is approximately three times faster for 55 ≤ HR < 80 and approximately two times faster for 80 ≤ HR < 105 and 80 ≤ HR < 105 than the existing IPI-based RBS generation techniques. Therefore, this study has got real-time significance for smart healthcare applications.
Similar content being viewed by others
References
Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: a review. In: Computer methods and programs in biomedicine
Wu W, Pirbhulal S, Sangaiah AK, Mukhopadhyay SC, Li G (2018) Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications. In: Future generation computer systems, pp 1–19
Pirbhulal S, Zhang H, Wu W (2017) HRV-based biometric privacy-preserving and security mechanism for wireless body sensor networks, 2017. In: Mukhopadhyay SC, Islam T (eds) Wearable sensors applications, design and implementation, vol 12. Springer, Berlin, pp 1–25
Pirbhulal S, Zhang H, Mukhopadhyay SC, Wu W, Zhang YT (2018) Heart-beats based biometric random binary sequences generation to secure wireless body sensor networks. IEEE Trans Biomed Eng 18(1): 1–9. http://ieeexplore.ieee.org/document/8314739
Pirbhulal S, Zhang H, Mukhopadhyay SC et al (2015) An efficient biometric-based algorithm using heart rate variability for securing body sensor networks. Sensors 15(7):15067–15089
The European Parliament and the Council of The European Union (2002) Directive 2002/58/EC concerning the processing of personal data and the protection of privacy in the electronic communications sector. Off J Eur Commun L201:37–47
Francillon A, Castelluccia C (2007) TinyRNG: a cryptographic random number generator for wireless sensors network nodes. In: Proceedings of 5th international symposium modeling optimization mobile, ad hoc wireless networks workshops (WiOpt), Limassol, Cyprus, pp 1–7
Lo Re G, Milazzo F, Ortolani M (2015) Secure random number generation in wireless sensor networks. Concurr Comput Pract Exp 27(15):3842–3862
Zhang G-H, Poon C, Zhang Y-T (2012) Analysis of using interpulse intervals to generate 128-bit biometric random binary sequences for securing wireless body sensor networks. IEEE Trans Inf Technol Biomed 16(1):176–182
Bao S-D, Poon CC, Zhang Y-T, Shen L-F (2008) Using the timing information of heartbeats as an entity identifier to secure body sensor network. IEEE Trans Inf Technol Biomed 12(6):772–779
Seepers RM, Strydis C, Sourdis I et al (2014) Adaptive entity-identifier generation for IMD emergency access. In: Proceedings of the first workshop on cryptography and security in computing systems, ACM, pp 41–44
Hu C, Cheng X, Zhang F et al (2013) OPFKA: secure and efficient ordered-physiological-feature-based key agreement for wireless body area networks. In: Proceedings 32nd IEEE international conference on computer communications, pp 2274–2282
Poon CC, Zhang Y-T, Bao S-D (2006) A novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health. IEEE Commun Mag 44(4):73–81
Xu F, Qin Z, Tan CC, Wang B, Li Q (2011) IMDGuard: securing implantable medical devices with the external wearable guardian. In: Proceedings of IEEE conference on computer communications, pp 1862–1870
Rukhin A, Soto J, Nechvatal J, Smid M, Barker E (2011) A statistical test suite for random and pseudorandom number generators for cryptographic applications, vol 1. Booz-Allen Hamilton Inc, Mclean, pp 1–164
Venkatasubramanian K, Banerjee A, Gupta S (2008) EKG-based key agreement in body sensor networks. In: IEEE INFOCOM workshops, pp 1–6
Gope P, Hwang T (2016) BSN-care: a secure IoT-based modern healthcare system using body sensor network. IEEE Sens J 16(5):1368–1376
Pirbhulal S, Zhang H, Alahi MEE et al (2016) A novel secure IoT-based smart home automation system using a wireless sensor network. Sensors 17(1):69
Huang H, Gong T, Ye N et al (2017) Private and secured medical data transmission and analysis for wireless sensing healthcare system. IEEE Trans Ind Inf 13(3):1227–1237
Peng H, Tian Y, Kurths J et al (2017) Secure and energy-efficient data transmission system based on chaotic compressive sensing in body-to-body networks. IEEE Trans Biomed Circ Syst 11(3):558–573
Hu C, Li H, Huo Y et al (2016) Secure and efficient data communication protocol for wireless body area networks. IEEE Trans Multi-scale Comput Syst 2(2):94–107
Antelmi I et al (2004) Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease. AJC 93(3):81–385
Rodrígueza R, Mexicanob A, Bilac J, Cervantesd S, Ponceb R (2015) Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis. J Appl Res Technol 15(1):261–269
Zheng G, Fang G, Shankaran R et al (2017) Multiple ECG fiducial points-based random binary sequence generation for securing wireless body area networks. IEEE J Biomed Health Inf 21(3):655–663
Maurer UM (1990) A universal statistical test for random bit generators. In: Conference on the theory and application of cryptography, Springer, Berlin, pp 409–420
Rukhin A, Soto J, Nechvatal J, Barker E, Leigh S, Levenson M, Banks D, Heckert A, Dray J, Vo S (2010) Statistical test suite for random and pseudorandom number generators for cryptographic applications. NIST Spec Publ 10:1–10
Simion E (2015) The relevance of statistical tests in cryptography. IEEE Secur Priv 15:66–70
J. L. Moraes, M. X. Rocha, G. G. Vasconcelos, J. E. Vasconcelos Filho, and V. H. C. de Albuquerque, “Advances in Photopletysmography Signal Analysis for Biomedical Applications,” Sensors (Basel, Switzerland), vol. 18, 2018
Mahmoud MM, Rodrigues JJ, Ahmed SH, Shah SC, Al-Muhtadi JF, Korotaev VV, DeAlbuquerque VH (2018) Enabling technologies on cloud of things for smart healthcare. IEEE Access 6:31950–31967
Luz EJDS, Nunes TM, De Albuquerque VHC, Papa JP, Menotti D (2013) ECG arrhythmia classification based on optimum-path forest. Exp Syst Appl 40:3561–3573
de Albuquerque VHC, Nunes TM, Pereira DR, Luz EJDS, Menotti D, Papa JP et al (2018) Robust automated cardiac arrhythmia detection in ECG beat signals. Neural Comput Appl 29:679–693
Vishnoi U, Noll T (2012) Area-and energy-efficient CORDIC accelerators in deep sub-micron CMOS technologies. Adv Radio Sci 10:207–213
Vishnoi U, Noll TG (2013) Cross-layer optimization of QRD accelerators. In: 2013 Proceedings of the ESSCIRC (ESSCIRC), pp 263–266
Acknowledgements
This work was supported in part by the Shenzhen Governmental Basic Research Grant (JCYJ20160331185848286, JCYJ20150529164154046), the National Natural Science Foundation of China under grants (U1613222, U16132228, 61873349), the Guangdong Province Natural Science Fund (2016A030310129), the Guangzhou Science and Technology Planning Project (201704020079, 201803010093), CAS President’s International Fellowship for Visiting Scientists (2017VTA0011).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Rights and permissions
About this article
Cite this article
Wu, W., Pirbhulal, S. & Li, G. Adaptive computing-based biometric security for intelligent medical applications. Neural Comput & Applic 32, 11055–11064 (2020). https://doi.org/10.1007/s00521-018-3855-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-018-3855-9