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CloudSec: A Lightweight and Agile Approach to Secure Medical Image Transmission in the Cloud Computing Environment

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Abstract

In this paper, we have proposed a novel method CloudSec. It is a lightweight and agile image encryption method that combines a hashing function, multi-wing chaotic mapping system, and genetics algorithm. The proposed method offers significant benefits in terms of efficiency in data authentication. Additionally, our proposed method does complementary and internal scrambling in order to increase security of the image. Secret key with features is used as a seeding value to generate a key DNA image from a hyper-chaotic system to eliminate the correlation between adjacent pixels by chaotic sequence. The complex dynamic behavior of the chaotic map generates random sequences for image diffusion. The propagation of information between a permuted DNA image and a key DNA image can be achieved through the use of DNA operations. Simulation results show that the proposed method used to authenticate and secure medical images in health care information systems. Our results demonstrated that it is very effective and reliable to fully recover medical image in case of any attack. The proposed approach executes faster, generates a large key space, and provides better encryption than available state-of-the-art techniques. It also resists various attacks, such as differential and statistical attack. The proposed crypto system is suitable for real-time applications in cloud computing and can be integrated into an IoHT framework for secure medical image transmission.

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We have used public test image source: https://www.kaggle.com/datasets.

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Correspondence to Avijit Mondal.

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This article is part of the topical collection “Innovation in Smart Things: A Systems, Security, and AI Perspective” guest edited by Niranjan K. Ray, Prasanth Yanambaka and Rakesh Balabantaray.

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Mondal, A., Chatterjee, P.S. CloudSec: A Lightweight and Agile Approach to Secure Medical Image Transmission in the Cloud Computing Environment. SN COMPUT. SCI. 5, 237 (2024). https://doi.org/10.1007/s42979-023-02539-w

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