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

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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