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Lightweight Bio-Chaos Crypt to Enhance the Security of Biometric Images in Internet of Things Applications

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Abstract

In recent times, numerous Internet of Things (IoT) applications have begun to use biometric identity for authentication purposes. The integrity and confidentiality of biometric templates during storage and transmission is crucial as it contains key information on the physical identity of the users. Encryption is an effective template protection technique. However, most of the edge side gadgets in the IoT environment require lightweight encryption schemes due to constraints in available power and memory space. Conventional cryptosystems are expensive because of their complexity and multiple rounds for encryption. In the present work, a lightweight bio-cryptosystem is developed to ensure security while storing and transmitting biometric templates. The proposed bio-crypto architecture has three stages—key generation, confusion and diffusion. A two-dimensional logistic sine map is used for key generation. A novel method of diffusion using DNA encoding and ciphering is proposed to decrease the complexity of the encryption process considerably and achieve desirable integrity. Simulations and security analysis indicate that the proposed cryptosystem has sufficient level of security and robustness, involves lesser computational complexity and has the potential of satisfying the requirements of IoT applications.

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taken from FVC 2002. Second row shows its corresponding cipher image

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References.

  1. Hamidi, H. (2018). An approach to develop the smart health using internet of things and authentication based on biometric technology. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2018.09.024

    Article  Google Scholar 

  2. Wazid, M., Das, A. K., Hussain, R., Succi, G., & Rodrigues, J. J. P. C. (2019). Authentication in cloud-driven IoT-based big data environment: Survey and outlook. Journal of Systems Architecture, 97, 185–196. https://doi.org/10.1016/j.sysarc.2018.12.005

    Article  Google Scholar 

  3. Obaidat, M. S., Rana, S. P., & Maitra, T. (2018). Biometric Security and Internet of Things (IoT) Chapter 19 Biometric Security and Internet of Things (IoT). https://doi.org/10.1007/978-3-319-98734-7

  4. El, H. A., & Hosni, M. M. (2019). Secure IoT communications for smart healthcare monitoring system, 1–14.

  5. Gupta, S., Buriro, A., & Crispo, B. (2019). DriverAuth: A risk-based multi-modal biometric-based driver authentication scheme for ride-sharing platforms. Computers & Security, 83, 122–139. https://doi.org/10.1016/j.cose.2019.01.007

    Article  Google Scholar 

  6. Jain, A. K., Nandakumar, K., & Nagar, A. (2008). Biometric Template Security, 2008. https://doi.org/10.1155/2008/579416

  7. Punithavathi, P., & Geetha, S. (2019). Partial DCT-based cancelable biometric authentication with security and privacy preservation for IoT applications. Multimedia Tools and Applications, 78, 25487–25514.

    Article  Google Scholar 

  8. Han, F. (2007). Fingerprint images encryption via multi-scroll chaotic attractors. Applied Mathematics and Computation, 185, 931–939. https://doi.org/10.1016/j.amc.2006.07.030

    Article  MATH  Google Scholar 

  9. Bhatnagar, G., Wu, J., & Raman, B. (2012). Fractional dual tree complex wavelet transform and its application to biometric security during communication and transmission. Future Generation Computer Systems, 28(1), 254–267. https://doi.org/10.1016/j.future.2010.11.012

    Article  Google Scholar 

  10. Bhatnagar, G., & Wu, Q. M. J. (2014). Enhancing the transmission security of biometric images using chaotic encryption. Multimedia Systems. https://doi.org/10.1007/s00530-013-0323-3

    Article  Google Scholar 

  11. Studies, A., & Playitas, Z. (2013). Double hyperchaotic encryption for security in biometric systems. Nonlinear Dynamics and Systems Theory, 13(1), 55–68.

    MathSciNet  Google Scholar 

  12. Hsiao, H., & Lee, J. (2015). Fingerprint image cryptography based on multiple chaotic systems. Signal Processing, 113, 169–181. https://doi.org/10.1016/j.sigpro.2015.01.024

    Article  Google Scholar 

  13. Loukhaoukha, K., Refaey, A., Zebbiche, K., & Shami, A. (2018). Efficient and secure cryptosystem for fingerprint images in wavelet domain. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-017-4938-9

    Article  Google Scholar 

  14. Rajendran, S., & Doraipandian, M. (2018). Biometric template security triggered by two dimensional logistic sine map. Procedia Computer Science, 143, 794–803.

    Article  Google Scholar 

  15. Hikal, N. A., & Eid, M. M. (2020). A new approach for palmprint image encryption based on hybrid chaotic maps. Journal of King Saud University - Computer and Information Sciences, 32(7), 870–882.

    Article  Google Scholar 

  16. Enayatifar, R., Sadaei, H. J., Abdullah, A. H., Lee, M., & Isnin, I. F. (2015). A novel chaotic based image encryption using a hybrid model of deoxyribonucleic acid and cellular automata. Optics and Lasers in Engineering, 71, 33–41. https://doi.org/10.1016/j.optlaseng.2015.03.007

    Article  Google Scholar 

  17. Liu, Y., Wang, J., Fan, J., & Gong, L. (2016). Image encryption algorithm based on chaotic system and dynamic S-boxes composed of DNA sequences. Multimedia Tools and Applications, 75(8), 4363–4382. https://doi.org/10.1007/s11042-015-2479-7

    Article  Google Scholar 

  18. Chai, X., Gan, Z., Yang, K., Chen, Y., & Liu, X. (2017). An image encryption algorithm based on the memristive hyperchaotic system, cellular automata and DNA sequence operations. Signal Processing : Image Communication, 52(November 2016), 6–19. https://doi.org/10.1016/j.image.2016.12.007

    Article  Google Scholar 

  19. Girdhar, A., & Kumar, V. (2018). A RGB image encryption technique using Lorenz and Rossler chaotic system on DNA sequences. Multimedia Tools and Applications, 77(20), 27017–27039. https://doi.org/10.1007/s11042-018-5902-z

    Article  Google Scholar 

  20. Yoosefian, D. N., Safdarian, N., & Hoseini Zadeh, S. A. (2020). New method for fingerprint images encryption using DNA sequence and chaotic tent map. Optik, 224(September), 165661. https://doi.org/10.1016/j.ijleo.2020.165661

    Article  Google Scholar 

  21. Vijayakumar, S., Jansi, M., Lavanya, D., & Lavanya Sree, V. (2020). Delay efficient genetic algorithm with DNA based cryptography for fingerprint authentication. European Journal of Molecular and Clinical Medicine, 7(4), 2077–2080.

    Google Scholar 

  22. Rajendran, S., Krithivasan, K., & Doraipandian, M. (2020). Fast pre-processing hex Chaos triggered color image cryptosystem. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-019-08396-1

    Article  Google Scholar 

  23. Soni, R., Johar, A., & Soni, V. (2013). An encryption and decryption algorithm for image based on DNA. In Proceedings—2013 international conference on communication systems and network technologies, CSNT 2013 (pp. 478–481). https://doi.org/10.1109/CSNT.2013.105

  24. Zhang, Q., & Wei, X. (2013). A novel couple images encryption algorithm based on DNAsubsequence operation and chaotic system. Optik, 124(23), 6276–6281. https://doi.org/10.1016/j.ijleo.2013.05.009

    Article  Google Scholar 

  25. Biometrics Ideal Test. (2020). http://biometrics.idealtest.org

  26. Sun, Z., Tan, T., Wang, Y., & Li, S. Z. (2005). Ordinal palmprint represention for personal identification [represention read representation]. In IEEE computer society conference on computer vision and pattern recognition (CVPR’05), San Diego, CA, USA, (Vol. 1, pp. 279–284). https://doi.org/10.1109/CVPR.2005.267

  27. Wu, J., Liao, X., & Yang, B. (2017). Color image encryption based on chaotic systems and elliptic curve ElGamal scheme. Signal Processing, 141, 109–124. https://doi.org/10.1016/j.sigpro.2017.04.006

    Article  Google Scholar 

  28. Chai, X., Fu, X., Gan, Z., Lu, Y., & Chen, Y. (2019). A color image cryptosystem based on dynamic DNA encryption and chaos. Signal Processing, 155, 44–62. https://doi.org/10.1016/j.sigpro.2018.09.029

    Article  Google Scholar 

  29. Han, C. (2019). An image encryption algorithm based on modified logistic chaotic map. Optik, 181(December 2018), 779–785. https://doi.org/10.1016/j.ijleo.2018.12.178

    Article  Google Scholar 

  30. Wu, J., Liao, X., & Yang, B. (2018). Image encryption using 2D Hénon-Sine map and DNA approach. Signal Processing, 153, 11–23. https://doi.org/10.1016/j.sigpro.2018.06.008

    Article  Google Scholar 

  31. Maddodi, G., Awad, A., Awad, D., Awad, M., & Lee, B. (2018). A new image encryption algorithm based on heterogeneous chaotic neural network generator and dna encoding. Multimedia Tools and Applications, 77, 24701–24725.

    Article  Google Scholar 

  32. Babaei, A., Motameni, H., & Enayatifar, R. (2020). A new permutation-diffusion-based image encryption technique using cellular automata and DNA sequence. Optik. https://doi.org/10.1016/j.ijleo.2019.164000

    Article  Google Scholar 

  33. Li, C., Luo, G., & Li, C. (2018). A parallel image encryption algorithm based on chaotic Duffing oscillators. Multimedia Tools and Applications, 77(15), 19193–19208. https://doi.org/10.1007/s11042-017-5391-5

    Article  Google Scholar 

  34. Karakiş, R., Güler, I., Çapraz, I., & Bilir, E. (2015). A novel fuzzy logic-based image steganography method to ensure medical data security. Computers in Biology and Medicine, 67, 172–183. https://doi.org/10.1016/j.compbiomed.2015.10.011

    Article  Google Scholar 

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Correspondence to Bhaskar Vidhyacharan.

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Sujarani, R., Manivannan, D., Manikandan, R. et al. Lightweight Bio-Chaos Crypt to Enhance the Security of Biometric Images in Internet of Things Applications. Wireless Pers Commun 119, 2517–2537 (2021). https://doi.org/10.1007/s11277-021-08342-1

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