Neural-assisted image-dependent encryption scheme for medical image cloud storage

Lakshmi, C.; Thenmozhi, K.; Rayappan, John Bosco Balaguru; Rajagopalan, Sundararaman; Amirtharajan, Rengarajan; Chidambaram, Nithya

doi:10.1007/s00521-020-05447-9

Cited by 41 publications

(21 citation statements)

References 37 publications

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“…Ten studies adopted statistical analysis mythologies. For example, Lakshmi et al [ 39 ] adopted correlation, entropy, and histogram analyses to validate the statistical resistivity of a Hopfield neural network (HNN)-driven image-dependent encryption framework for storing medical images on the cloud. In Ref.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, recurrent HNN and back-propagation neural networks are mainly used for medical data storage and publishing through medical imaging in Web information management. Lakshmi et al [ 39 ] proposed HNN-integrated image encryption technologies to deal with a variety of attacks via continuous learning and updating, in which the back-propagation neural network generated “image-specific keys that increased the resiliency against hackers and then the generated keys were used as an initial seed for confusion and diffusion sequence generation through HNN (p. 6671)”.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

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Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

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show abstract

“…Furthermore, the high sensitivity of the key (due to the chaotic maps and the hash functions) provides a sufficiently high level of security against these attacks. To ensure robustness against CPA, we test our scheme using XOR‐based substitution methods [57]. The scheme is considered secure when satisfying the following equation:

1 \oplus I_{2} \neq C I_{1} \oplus C I_{2},

where I 1 and I 2 are the plain images, and

C I_{1}

and

C I_{2}

are their corresponding ciphered images.…”

Section: Resultsmentioning

confidence: 99%

Robustly correlated key‐medical image for DNA‐chaos based encryption

Aouissaoui

Bakir

Sakly

2021

IET Image Processing

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Medical images include confidential and sensitive information about patients. Hence, ensuring the security of these images is a crucial requirement. This paper proposes an efficient and secure medical image encryption-decryption scheme based on deoxyribonucleic acid (DNA), one-dimensional chaotic maps (tent and logistic maps), and hash functions (SHA-256 and MD5). The first part of the proposed scheme is the key generation based on the hash functions of the image and its metadata. The key then is highly related and intensely sensitive to the original image. The second part is the rotation and permutation of the first two MSB bit-plans of the medical image to reduce its black background that produces redundant DNA encoded sequences. The third part is the DNA encoding-decoding using dynamically chosen DNA rules for every 2-bit pixel value through the logistic map. Meanwhile, the confusion-diffusion is performed using the tent map and XOR operation. Simulation results and security analysis prove the good encryption effects of the proposed scheme compared to the state-of-art methods with a correlation of 6.66617e-7 and a very large key space of 2 624 . Furthermore, the proposed system has a strong ability to resist various common attacks such as chosen/known-plaintext attacks and cropping/noise attacks. Recently, there has been a significant interest in chaosbased cryptography because of its interesting features such asThis 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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“…It needs a large storage for storing the medical image. For addressing this issue, in 2020, Lakshmi et al [44] proposed a medical image encryption method in a cloud platform based on HNN (Hopfield neural network). e method ensures improved security than the existing methods.…”

Section: Literature Studymentioning

confidence: 99%

Chaotic Lightweight Cryptosystem for Image Encryption

Ferdush¹,

Begum

Uddin

2021

Advances in Multimedia

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Data transmission over the Internet and the personal network has been risen day by day due to the advancement of multimedia technology. Hence, it is today’s prime concern to protect the data from unauthorized access and encrypt the multimedia element as they are stored on the web servers and transmitted over the networks. Therefore, multimedia data encryption is essential. But, the multimedia encryption algorithm is complex to implement as it requires more time and memory space. For this reason, the lightweight image encryption algorithm gains popularity that requires less memory and less time along with low power or energy and provides supreme security for limited devices. In this study, we have studied the chaotic-based lightweight image encryption method. At first, we have presented a standard framework and algorithm based on two chaotic maps such as Arnold and logistic for lightweight image encryption and performed some experiments. We have analyzed different groups of images such as miscellaneous, medical, underwater, and texture. Experimentations have provided the largest entropy 7.9920 for medical image (chest X-ray), large key space 2m×m×8, and average encryption and decryption times are 3.9771 s and 3.1447 s, respectively. Besides, we have found an equal distribution of pixels and less correlation coefficients among adjacent pixels of the encrypted image. These criteria indicate an efficient image encryption method. Also, our method is efficient and less complex than the existing state-of-the-art methods.

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Neural-assisted image-dependent encryption scheme for medical image cloud storage

Cited by 41 publications

References 37 publications

Machine and cognitive intelligence for human health: systematic review

Machine and cognitive intelligence for human health: systematic review

Robustly correlated key‐medical image for DNA‐chaos based encryption

Chaotic Lightweight Cryptosystem for Image Encryption

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