Medical image encryption based on biometric keys and lower–upper decomposition with partial pivoting

Wang, Xiaohui; Zhu, Zheng; Wang, Fan; Ni, Renjie; Wang, Jun; Hu, Yu-Hen

doi:10.1364/ao.410329

Cited by 13 publications

(4 citation statements)

References 46 publications

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“…Additionally, the secret key is usually meaningless and has nothing to do with the user. If the secret key is lost or stolen, anyone can use the key for decryption [30]. In order to address these issues, this paper proposes the use of biometric images, such as face, iris, fingerprint and palmprint (i.e., figures 1(a)-(d)), as secret keys.…”

Section: Biometric Key and Its Equivalent Keystreammentioning

confidence: 99%

3D medical image encryption algorithm using biometric key and cubic S-box

Liu,

Xue

2024

Phys. Scr.

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Considering the scarcity of research on 3D medical image encryption, this paper proposes a novel 3D medical image encryption scheme based on biometric key and cubic S-box. To enhance the data security, biometric keys are utilized to overcome the limitations of traditional methods where secret keys with no practical meaning, fixed length, and finite key space, while cubic S-box is constructed to increase the nonlinearity of image cryptosystem. The proposed cryptosystem mainly consists of four phases: pseudo-random sequence generation, confusion, substitution, and diffusion. Firstly, the stepwise iterative algorithm based on coupled chaotic systems is utilized for generating pseudo-random sequences for confusion and diffusion. Secondly, the confusion algorithm based on multiple sorting can scramble pixel positions in 3D images. Thirdly, guided by the designed cubic S-box, pixel substitution is executed sequentially. Lastly, the diffusion algorithm based on ECA and finite field multiplication is capable of increasing the plaintext sensitivity of cryptosystem by concealing the statistical characteristics of plaintext. Simulation experiments performed on multiple 3D medical images demonstrate that the proposed encryption scheme exhibits favorable statistical performance, sufficiently large key space, strong system sensitivity and robustness, and can resist various typical cryptographic attacks.

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Section: Biometric Key and Its Equivalent Keystreammentioning

confidence: 99%

3D medical image encryption algorithm using biometric key and cubic S-box

Liu,

Xue

2024

Phys. Scr.

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“…The author proposed a new encryption technique based on the parameterized all-phase orthogonal transformation. In [32], the iris is used to generate two spiral phase masks. They are used with the lower-upper decomposition with partial pivoting to encrypt the medical image.…”

Section: Related Workmentioning

confidence: 99%

A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission

2023

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Secure transmission of medical images and medical data is essential in healthcare systems, both in telemedicine and AI approaches. The compromise of images and medical data could affect patient privacy and the accuracy of diagnosis. Digital watermarking embeds medical images into a non-significant image before transmission to ensure visual security. However, it is vulnerable to white-box attacks because the embedded medical image can be extracted by an attacker that knows the system’s operation and does not ensure the authenticity of image transmission. A visually secure image encryption scheme for secure fingerprint-based authenticated transmission has been proposed to solve the above issues. The proposed scheme embeds the encrypted medical image, the encrypted physician’s fingerprint, and the patient health record (EHR) into a non-significant image to ensure integrity, authenticity, and confidentiality during the medical image and medical data transmission. A chaotic encryption algorithm based on a permutation key has been used to encrypt the medical image and fingerprint feature vector. A hybrid asymmetric cryptography scheme based on Elliptic Curve Cryptography (ECC) and AES has been implemented to protect the permutation key. Simulations and comparative analysis show that the proposed scheme achieves higher visual security of the encrypted image and higher medical image reconstruction quality than other secure image encryption approaches.

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“…However, once the image is decrypted, the image is no longer protected [15][16][17]. Although current medical image encryption algorithms play strong roles in protecting the security of medical images, the security level of medical images is still limited because medical images may involve the privacy of many patients, some of which are confidential and sensitive [18]. In [19], a medical image encryption algorithm based on the Goldreich-Goldwasser-Halevi encryption scheme was studied.…”

Section: Introductionmentioning

confidence: 99%

“…That algorithm exhibits strong robustness and the ability to resist statistical and differential attacks. In [18], an asymmetric medical image encryption scheme was proposed with an iris for medical image encryption to improve the security of the encryption scheme. Second, asymmetric optical encryption technology was combined with three-dimensional Lorenz chaos technology to improve the key space and solve the linear problem based on double random phase coding.…”

Section: Introductionmentioning

confidence: 99%

Medical image encryption scheme based on self‐verification matrix

Man

2021

IET Image Processing

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To mitigate the shortcomings of existing medical image encryption algorithms, including a lack of anti-tampering methods and security, this report presents an anti-tampering encryption algorithm for medical images that is based on a self-verification matrix. First, chaotic coordinates generated by chaos are used to traverse all pixels in a plain image to generate a two-dimensional matrix (a self-verification matrix) with positioning information. The accurate location of illegally altered image pixels can be detected using the self-verification matrix. To improve the security of the self-authentication matrix, DNA coding is also applied to the self-authentication matrix, and the plain image is also diffused statically to destroy the pixel distribution. Next, the scrambled image and self-verification matrix are mixed and cross-scrambled. Finally, the fused image is diffused dynamically to improve the security of the encrypted image. Experimental simulation and performance analysis show that the algorithm achieves good encryption effectiveness, provides strong anti-tampering capabilities, and can accurately locate at least 4 pixels.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Medical image encryption based on biometric keys and lower–upper decomposition with partial pivoting

Cited by 13 publications

References 46 publications

3D medical image encryption algorithm using biometric key and cubic S-box

3D medical image encryption algorithm using biometric key and cubic S-box

A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission

Medical image encryption scheme based on self‐verification matrix

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