International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Enhanced Fractional Order Lorenz System for Medical Image Encryption in Cloud-Based Healthcare Administration


Affiliations
1 Department of Computer Science, College of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India
   Untitled

Cloud technology is a new computing paradigm increasing at a frenetic pace. Recently, doctors have switched to cloud computing as it provides wide storage spaces. A medical image highlights the patient's physical condition. These medical images possess a stronger correlation and larger data volume than ordinary images. Moreover, the current image encryption methodologies have several limitations during the encryption of medical images. This paper enhances the Fractional Order Lorenz System and a Matrix Scrambling Method (FOLS-MSM) for achieving medical image encryption with maximized correlation, reliability, and high resolution. In particular, the Fractional Order Lorenz System is developed by integrating the potentialities of the Arnold map, Tent map, and Lorenz Map for attaining the image encryption process. Initially, the Arnold map is used for scrambling the initial value. Then, the tent map is used iteratively to determine the state values to locate the position of the plaintext pixel. Then, the fractional Lorenz system considers the moulded pixel as the input, and scrambling is attained using a matrix method to attain confusion. Moreover, it generates the pseudo-random sequence for performing the cross-diffusion process to obtain the encrypted image. The potentiality of the enhanced FOLS-MSM explored based on security analysis with respect to sensitivity, correlation, PSNR, key space, histogram, and entropy analysis confirmed its predominance over the baseline medical encryption schemes used for comparison.

Keywords

Medical Images, Encryption, Fractional Order Lorenz System, Matrix Scrambling Method, Pseudo-Random Sequence, Tent Map, Arnold Map.
User
Notifications
Font Size

  • Y. Gong, C. Zhang, Y. Fang, and J. Sun, “Protecting Location Privacy for Task Allocation in Ad Hoc Mobile Cloud Computing,” IEEE Trans. Emerg. Top. Comput., vol. 6, no. 1, pp. 110–121, 2018, doi: 10.1109/TETC.2015.2490021.

  • W. Li et al., “Unified Fine-Grained Access Control for Personal Health Records in Cloud Computing,” IEEE J. Biomed. Heal. Informatics, vol. 23, no. 3, pp. 1278–1289, 2019, doi: 10.1109/JBHI.2018.2850304.

  • X. Li, J. Yuan, H. Ma, and W. Yao, “Fast and Parallel Trust Computing Scheme Based on Big Data Analysis for Collaboration Cloud Service,” IEEE Trans. Inf. Forensics Secur., vol. 13, no. 8, pp. 1917–1931, 2018, doi: 10.1109/TIFS.2018.2806925.

  • D. S. Laiphrakpam and M. S. Khumanthem, “Medical image encryption based on improved ElGamal encryption technique,” Optik (Stuttg)., vol. 147, pp. 88–102, 2017, doi: 10.1016/j.ijleo.2017.08.028.

  • R. Enayatifar, A. H. Abdullah, and M. Lee, “A weighted discrete imperialist competitive algorithm (WDICA) combined with chaotic map for image encryption,” Opt. Lasers Eng., vol. 51, no. 9, pp. 1066–1077, 2013, doi: 10.1016/j.optlaseng.2013.03.010.

  • D. Ravichandran, P. Praveenkumar, J. B. Balaguru Rayappan, and R. Amirtharajan, “Chaos based crossover and mutation for securing DICOM image,” Comput. Biol. Med., vol. 72, pp. 170–184, 2016, doi: 10.1016/j.compbiomed.2016.03.020.

  • D. He, N. Kumar, M. K. Khan, L. Wang, and J. Shen, “Efficient Privacy-Aware Authentication Scheme for Mobile Cloud Computing Services,” IEEE Syst. J., vol. 12, no. 2, pp. 1621–1631, 2018, doi: 10.1109/JSYST.2016.2633809.

  • Z. Deng and S. Zhong, “A digital image encryption algorithm based on chaotic mapping,” J. Algorithms Comput. Technol., vol. 13, pp. 1–11, 2019, doi: 10.1177/1748302619853470.

  • J. Zhao, S. Wang, Y. Chang, and X. Li, “A novel image encryption scheme based on an improper fractional-order chaotic system,” Nonlinear Dyn., vol. 80, no. 4, pp. 1721–1729, 2015, doi: 10.1007/s11071-015-1911-x.

  • Q. Lu, C. Zhu, and X. Deng, “An Efficient Image Encryption Scheme Based on the LSS Chaotic Map and Single S-Box,” IEEE Access, vol. 8, pp. 25664–25678, 2020, doi: 10.1109/ACCESS.2020.2970806.

  • G. Chen, Y. Mao, and C. K. Chui, “A symmetric image encryption scheme based on 3D chaotic cat maps,” Chaos, Solitons and Fractals, vol. 21, no. 3, pp. 749–761, 2004, doi: 10.1016/j.chaos.2003.12.022.

  • Y. Dai, H. Wang, and Y. Wang, “Chaotic Medical Image Encryption Algorithm Based on Bit-Plane Decomposition,” Int. J. Pattern Recognit. Artif. Intell., vol. 30, no. 4, pp. 1–15, 2016, doi: 10.1142/S0218001416570019.

  • S. Ibrahim et al., “Framework for Efficient Medical Image Encryption Using Dynamic S-Boxes and Chaotic Maps,” IEEE Access, vol. 8, pp. 160433–160449, 2020, doi: 10.1109/ACCESS.2020.3020746.

  • J. C. Dagadu, J. P. Li, and E. O. Aboagye, “Medical Image Encryption Based on Hybrid Chaotic DNA Diffusion,” Wirel. Pers. Commun., vol. 108, no. 1, pp. 591–612, 2019, doi: 10.1007/s11277-019-06420-z.

  • M. Gafsi, N. Abbassi, M. A. Hajjaji, J. Malek, and A. Mtibaa, “Improved chaos-based cryptosystem for medical image encryption and decryption,” Sci. Program., vol. 2020, 2020, doi: 10.1155/2020/6612390.

  • K. Jain, A. Aji, and P. Krishnan, “Medical Image Encryption Scheme Using Multiple Chaotic Maps,” Pattern Recognit. Lett., vol. 152, pp. 356–364, 2021, doi: 10.1016/j.patrec.2021.10.033.

  • Z. Liang, Q. Qin, C. Zhou, N. Wang, Y. Xu, and W. Zhou, Medical image encryption algorithm based on a new five-dimensional three-leaf chaotic system and genetic operation, vol. 16, no. 11 November. 2021.

  • M. Z. Talhaoui and X. Wang, “A new fractional one dimensional chaotic map and its application in high-speed image encryption,” Inf. Sci. (Ny)., vol. 550, pp. 13–26, 2021, doi: https://doi.org/10.1016/j.ins.2020.10.048.

  • A. Girdhar, H. Kapur, and V. Kumar, “A novel grayscale image encryption approach based on chaotic maps and image blocks,” Appl. Phys. B Lasers Opt., vol. 127, no. 3, 2021, doi: 10.1007/S00340-021-07585-X.

  • J. Ferdush, M. Begum, and M. S. Uddin, “Chaotic Lightweight Cryptosystem for Image Encryption,” Adv. Multimed., vol. 2021, p. 5527295, 2021, doi: 10.1155/2021/5527295.

  • M. Lyle, P. Sarosh, and S. A. Parah, “Adaptive image encryption based on twin chaotic maps,” Multimed. Tools Appl., vol. 81, no. 6, pp. 8179–8198, 2022, doi: 10.1007/s11042-022-11917-0.

  • S. Zhu, G. Wang, and C. Zhu, “A Secure and Fast Image Encryption Scheme Based on Double Chaotic S-Boxes,” Entropy , vol. 21, no. 8. 2019, doi: 10.3390/e21080790.

  • H. Zhong and G. Li, “Multi-image encryption algorithm based on wavelet transform and 3D shuffling scrambling,” Multimed. Tools Appl., 2022, doi: 10.1007/s11042-022-12479-x.

  • Y. Liu, J. Zhang, D. Han, P. Wu, Y. Sun, and Y. S. Moon, “A multidimensional chaotic image encryption algorithm based on the region of interest,” Multimed. Tools Appl., vol. 79, no. 25, pp. 17669–17705, 2020, doi: 10.1007/s11042-020-08645-8.

  • M. A. Ben Farah, A. Farah, and T. Farah, “An image encryption scheme based on a new hybrid chaotic map and optimized substitution box,” Nonlinear Dyn., vol. 99, no. 4, pp. 3041–3064, 2020, doi: 10.1007/s11071-019-05413-8.

  • A. Mansouri and X. Wang, “A novel one-dimensional sine powered chaotic map and its application in a new image encryption scheme,” Inf. Sci. (Ny)., vol. 520, pp. 46–62, 2020, doi: https://doi.org/10.1016/j.ins.2020.02.008.

  • C. Zou, Q. Zhang, X. Wei, and C. Liu, “Image Encryption Based on Improved Lorenz System,” IEEE Access, vol. 8, pp. 75728–75740, 2020, doi: 10.1109/ACCESS.2020.2988880.

  • R. Vidhya and M. Brindha, “A novel conditional Butterfly Network Topology based chaotic image encryption,” J. Inf. Secur. Appl., vol. 52, p. 102484, 2020, doi: https://doi.org/10.1016/j.jisa.2020.102484.

  • R. I. Abdelfatah, “A new fast double-chaotic based Image encryption scheme,” Multimed. Tools Appl., vol. 79, no. 1, pp. 1241–1259, 2020, doi: 10.1007/s11042-019-08234-4.

  • M. Z. Talhaoui, X. Wang, and A. Talhaoui, “A new one-dimensional chaotic map and its application in a novel permutation-less image encryption scheme,” Vis. Comput., vol. 37, no. 7, pp. 1757–1768, 2021, doi: 10.1007/s00371-020-01936-z.

  • M. T. Elkandoz and W. Alexan, “Image encryption based on a combination of multiple chaotic maps,” Multimed. Tools Appl., 2022, doi: 10.1007/s11042-022-12595-8.

  • S. K.U. and A. Mohamed, “Novel hyper chaotic color image encryption based on pixel and bit level scrambling with diffusion,” Signal Process. Image Commun., vol. 99, p. 116495, 2021, doi: https://doi.org/10.1016/j.image.2021.116495.

  • “E. N. Lorenz, ‘“Deterministic nonperiodic flow,”’J. Atmos. Sci., vol. 20,no. 2, pp. 130–141, 1963.”

  • S. Li, L. Zhao, and N. Yang, “Medical Image Encryption Based on 2D Zigzag Confusion and Dynamic Diffusion,” Secur. Commun. Networks, vol. 2021, 2021, doi: 10.1155/2021/6624809.

  • F. Masood et al., “A Lightweight Chaos-Based Medical Image Encryption Scheme Using Random Shuffling and XOR Operations,” Wirel. Pers. Commun., no. 0123456789, 2021, doi: 10.1007/s11277-021-08584-z.

  • S. Dhall, S. K. Pal, and K. Sharma, “A chaos-based probabilistic block cipher for image encryption,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 1, pp. 1533–1543, 2022, doi: https://doi.org/10.1016/j.jksuci.2018.09.015.

  • M. Alawida, A. Samsudin, J. Sen Teh, and R. S. Alkhawaldeh, “A new hybrid digital chaotic system with applications in image encryption,” Signal Processing, vol. 160, pp. 45–58, 2019, doi: https://doi.org/10.1016/j.sigpro.2019.02.016.

  • D. R. I. M. Setiadi, E. H. Rachmawanto, and R. Zulfiningrum, “Medical Image Cryptosystem using Dynamic Josephus Sequence and Chaotic-hash Scrambling,” J. King Saud Univ. - Comput. Inf. Sci., 2022, doi: https://doi.org/10.1016/j.jksuci.2022.04.002.


Abstract Views: 274

PDF Views: 1 PDF Views: 0




  • Enhanced Fractional Order Lorenz System for Medical Image Encryption in Cloud-Based Healthcare Administration

Abstract Views: 274  |  PDF Views: 1 PDF Views: 0

Authors

P. Suhasini
Department of Computer Science, College of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India
S. Kanchana
Department of Computer Science, College of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India

Abstract


Cloud technology is a new computing paradigm increasing at a frenetic pace. Recently, doctors have switched to cloud computing as it provides wide storage spaces. A medical image highlights the patient's physical condition. These medical images possess a stronger correlation and larger data volume than ordinary images. Moreover, the current image encryption methodologies have several limitations during the encryption of medical images. This paper enhances the Fractional Order Lorenz System and a Matrix Scrambling Method (FOLS-MSM) for achieving medical image encryption with maximized correlation, reliability, and high resolution. In particular, the Fractional Order Lorenz System is developed by integrating the potentialities of the Arnold map, Tent map, and Lorenz Map for attaining the image encryption process. Initially, the Arnold map is used for scrambling the initial value. Then, the tent map is used iteratively to determine the state values to locate the position of the plaintext pixel. Then, the fractional Lorenz system considers the moulded pixel as the input, and scrambling is attained using a matrix method to attain confusion. Moreover, it generates the pseudo-random sequence for performing the cross-diffusion process to obtain the encrypted image. The potentiality of the enhanced FOLS-MSM explored based on security analysis with respect to sensitivity, correlation, PSNR, key space, histogram, and entropy analysis confirmed its predominance over the baseline medical encryption schemes used for comparison.

Keywords


Medical Images, Encryption, Fractional Order Lorenz System, Matrix Scrambling Method, Pseudo-Random Sequence, Tent Map, Arnold Map.

References





DOI: https://doi.org/10.22247/ijcna%2F2022%2F214504