Medical image encryption scheme based on self‐verification matrix

Man, Zhenlong; Di, Xiaoqiang

doi:10.1049/ipr2.12263

Cited by 10 publications

(4 citation statements)

References 57 publications

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“…Compared with the correlation coefficient of the medical image in Table 14 , the correlation coefficient of the encrypted image obtained by this algorithm is slightly lower than the [ 47 , 48 ], and slightly higher than the [ 49 ]. Compared with the correlation coefficient of ordinary images in Table 15 , the correlation coefficient of the encrypted image obtained by this algorithm is slightly lower than that of [ 1 ], and slightly higher than that of [ 33 , 54 ].…”

Section: Simulation Results and Security Analysismentioning

confidence: 79%

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Medical image encryption algorithm based on a new five-dimensional three-leaf chaotic system and genetic operation

et al. 2021

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Current image encryption methods have many shortcomings for the medical image encryption with high resolution, strong correlation and large storage space, and it is difficult to obtain reliable clinically applicable medical images. Therefore, this paper proposes a medical image encryption algorithm based on a new five-dimensional three-leaf chaotic system and genetic operation. And the dynamic analysis of the phase diagram and bifurcation diagram of the five-dimensional three-leaf chaotic system selected in this paper is carried out, and NIST is used to test the randomness of its chaotic sequence. This algorithm follows the diffusion-scrambling framework, especially using the principle of DNA recombination combined with the five-dimensional three-leaf chaotic system to generate a chaotic matrix that participates in the operation. The bit-level DNA mutation operation is introduced in the diffusion, and the scrambling and diffusion effects have been further improved. Algorithm security and randomness have been enhanced. This paper evaluates the efficiency of this algorithm for medical image encryption in terms of security analysis and time performance. Security analysis is carried out from key space, information entropy, histogram, similarity between decrypted image and original image, PSNR, correlation, sensitivity, noise attack, cropping attack and so on. Perform time efficiency analysis from the perspective of time performance. The comparison between this algorithm and the experimental results obtained by some of the latest medical image encryption algorithms shows that this algorithm is superior to the existing medical image encryption algorithms to a certain extent in terms of security and time efficiency.

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Section: Simulation Results and Security Analysismentioning

confidence: 79%

“…In the Table 18 , use MI instead of medical image. It can be seen from Table 18 that the encryption and decryption time of this algorithm is slightly longer than that of [ 48 ], which is slightly shorter than the other 3 references. The encryption and decryption time can be controlled at about 1 second.…”

Section: Simulation Results and Security Analysismentioning

confidence: 87%

Medical image encryption algorithm based on a new five-dimensional three-leaf chaotic system and genetic operation

et al. 2021

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“…In recent years, researchers have facilitated the transmission of images through image encryption [6][7][8], image watermarking [9] and data hiding [10] methods. Among them, image encryption is the most direct and effective way to protect images.…”

Section: Introductionmentioning

confidence: 99%

Selective region medical image encryption algorithm based on cascade chaos and two-dimensional Joseph traversal

Chen

Li²,

Lin³

et al. 2023

Phys. Scr.

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Aiming at the problem that medical image information is easy to be stolen, tampered and maliciously destroyed in the transmission process, a selective region medical image encryption algorithm based on cascade chaos is proposed. The encryption algorithm consists of two stages: region of interest (ROI) encryption and full image encryption. First, the Cubic map is cascaded with an improved Cosine map. Through the analysis of dynamic characteristics, the cascade map has better chaotic performance than the traditional chaotic systems. The key sequence is generated by the cascade map and the image. Then edge detection is performed on the image to determine ROI. ROI is divided into multiple image blocks, and a new gradient algorithm is used to label the sub-blocks containing important information. A modified two-dimensional Joseph traversal method is used to scramble the positions of pixels in each sub-block, and further modify their pixel values with a round of diffusion. Finally, we use a block V-shape scrambling method to scramble the entire image. The final encrypted image is obtained through a round of bidirectional diffusion of all pixels using chaotic sequences. Simulation results and security analysis show that the algorithm has large key space and sufficient security. It can effectively resist various malicious attacks.

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“…With the rapid generation of voluminous data from diverse smart information sources, private and public corporations face the challenge of effectively managing big data throughout its life cycle to extract value from it [9,10]. Data security is also an essential part of its life cycle, and machine-learning-based encryption algorithms have been presented as well to safeguard data from security breaches and threats [11,12]. In recent years, data-driven organizations in the public and private sectors have developed data strategies and policies aligned with their goals and mission.…”

Section: Introductionmentioning

confidence: 99%

Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design and Development Perspective

Zahid

Altaf

Ahmad

et al. 2023

Systems

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The rapid generation of data from various sources by the public sector, private corporations, business associations, and local communities is referred to as big data. This large and complex dataset is often regarded as the ‘new oil’ by public administrations (PAs), and data-driven approaches are employed to transform it into valuable insights that can improve governance, transparency, digital services, and public engagement. The government’s big-data ecosystem (GBDE) is a result of this initiative. Effective data management is the first step towards large-scale data analysis, which yields insights that benefit your work and your customers. However, managing big data throughout its life cycle is a daunting challenge for public agencies. Despite its widespread use, big data management is still a significant obstacle. To address this issue, this study proposes a hybrid approach to secure the data management life cycle for GBDE. Specifically, we use a combination of the ECC algorithm with AES 128 BITS encryption to ensure that the data remain confidential and secure. We identified and analyzed various data life cycle models through a systematic literature review to create a data management life cycle for data-driven governments. This approach enhances the security and privacy of data management and addresses the challenges faced by public agencies.

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Medical image encryption scheme based on self‐verification matrix

Cited by 10 publications

References 57 publications

Medical image encryption algorithm based on a new five-dimensional three-leaf chaotic system and genetic operation

Medical image encryption algorithm based on a new five-dimensional three-leaf chaotic system and genetic operation

Selective region medical image encryption algorithm based on cascade chaos and two-dimensional Joseph traversal

Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design and Development Perspective

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