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Robust medical image encryption based on DNA-chaos cryptosystem for secure telemedicine and healthcare applications

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Abstract

The security of healthcare and telemedicine systems is a critical issue that must be significantly investigated. Several smart telemedicine applications are expected to be adopted in the medical sector in the incoming years. Healthcare smart products that are connected through Internet to be accessible anytime and anywhere are expected to deal with critical and confidential information such as personal medical images. Therefore, medical image encryption is an important task in telemedicine and healthcare applications. This paper presents an efficient cryptosystem for medical image security based on exploiting the advantages of the de-oxyribo nucleic acid (DNA) rules and chaos maps. In the proposed medical image cryptosystem, logistic chaos map, piecewise linear chaotic map (PWLCM), and DNA encoding are employed. The PWLCM is employed to generate a secret key image. Then, the DNA rules are utilized for encoding the secret key image and the input plain image by rows that are encoded with the logistic chaos map. After that, the proposed logistic map is employed to obtain an intermediate image as another secret key image to set DNA functions row-by-row on the coded original image. Moreover, the intermediate image is decoded in the following stage. Finally, the previous actions are iterated through image columns once again to obtain the best ciphered image. The experimental results reveal that the suggested cryptosystem has a high security with an acceptable processing time. In addition, it can resist various kinds of attacks, such as known-plaintext and chosen-plaintext attacks.

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Acknowledgement

The authors would like to thank Prince Sultan University for their support.

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Correspondence to Walid El-Shafai.

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El-Shafai, W., Khallaf, F., El-Rabaie, ES.M. et al. Robust medical image encryption based on DNA-chaos cryptosystem for secure telemedicine and healthcare applications. J Ambient Intell Human Comput 12, 9007–9035 (2021). https://doi.org/10.1007/s12652-020-02597-5

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