Chua's diode and strange attractor: a three‐layer hardware–software co‐design for medical image confidentiality

Rajagopalan, Sundararaman; Poori, Siva; Narasimhan, Mukund; Rethinam, Sivaraman; Kuppusamy, Chandrasekar Vallipalayam; Balasubramanian, Ramalingam; Annamalai, Vijaya Moorthi Paramasivam; Amirtharajan, Rengarajan

doi:10.1049/iet-ipr.2019.0562

Cited by 38 publications

(10 citation statements)

References 43 publications

(50 reference statements)

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“…Simulation results show that the methods are well hidden and provide good resistance against attacks during image handling operations. In [13], the authors present a proportional study of patient facts interspersed in the use of a manuscript signal of an electrocardiogram ECG in medical metaphors, using discrete Fourier transforms and DCT. For security, text information and ECG signals are encoded before being incorporated into the frequency state.…”

Section: Summary Of Related Workmentioning

confidence: 99%

Medical Image Transmission Using Novel Crypto-Compression Scheme

Mashat¹,

Bhatia²,

Kumar³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Section: Summary Of Related Workmentioning

confidence: 99%

Medical Image Transmission Using Novel Crypto-Compression Scheme

Mashat¹,

Bhatia²,

Kumar³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“…According to Shannon [11], an encryption scheme that consists of both a confusion and diffusion property can be considered a secured cryptosystem. Fridrich proposed a confusion-diffusion structure in [12] that encrypted images using the permuting pixel location and diffusing pixel value with chaos for the first time. However, as compared to permuting pixels, the permutation of bits of pixels gives more security, while the calculation cost may be larger.…”

Section: Data Securitymentioning

confidence: 99%

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model

Rehman

Shafique

Khan

et al. 2022

Sensors

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This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such as pneumonia is a challenging task for researchers. In the past few years, patients’ medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients’ medical records. Therefore, it is important to secure medical data, which are in the form of images. The proposed work is divided into two sections: in the first section, primary data in the form of images are encrypted using the proposed technique based on chaos and convolution neural network. Furthermore, multiple chaotic maps are incorporated to create a random number generator, and the generated random sequence is used for pixel permutation and substitution. In the second part of the proposed work, a new technique for pneumonia diagnosis using deep learning, in which X-ray images are used as a dataset, is proposed. Several physiological features such as cough, fever, chest pain, flu, low energy, sweating, shaking, chills, shortness of breath, fatigue, loss of appetite, and headache and statistical features such as entropy, correlation, contrast dissimilarity, etc., are extracted from the X-ray images for the pneumonia diagnosis. Moreover, machine learning algorithms such as support vector machines, decision trees, random forests, and naive Bayes are also implemented for the proposed model and compared with the proposed CNN-based model. Furthermore, to improve the CNN-based proposed model, transfer learning and fine tuning are also incorporated. It is found that CNN performs better than other machine learning algorithms as the accuracy of the proposed work when using naive Bayes and CNN is 89% and 97%, respectively, which is also greater than the average accuracy of the existing schemes, which is 90%. Further, K-fold analysis and voting techniques are also incorporated to improve the accuracy of the proposed model. Different metrics such as entropy, correlation, contrast, and energy are used to gauge the performance of the proposed encryption technology, while precision, recall, F1 score, and support are used to evaluate the effectiveness of the proposed machine learning-based model for pneumonia diagnosis. The entropy and correlation of the proposed work are 7.999 and 0.0001, respectively, which reflects that the proposed encryption algorithm offers a higher security of the digital data. Moreover, a detailed comparison with the existing work is also made and reveals that both the proposed models work better than the existing work.

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“…There are four classic types of attacks: cipher text only attacks, known-plaintext attacks, chosen-plaintext attacks, and chosen cipher text attacks. 34,35 The strongest is the chosen plaintext attack. Accordingly, if a cryptosystem resists the chosen plaintext attack, it can resist all other attacks.…”

Section: Chosen Plain Text Attack Analysismentioning

confidence: 99%

“…There are four classic types of attacks: cipher text only attacks, known‐plaintext attacks, chosen‐plaintext attacks, and chosen cipher text attacks 34,35 . The strongest is the chosen plaintext attack.…”

Section: Simulation and Security Analysismentioning

confidence: 99%

Improved chaos‐RSA‐based hybrid cryptosystem for image encryption and authentication

Gafsi

Amdouni

Hajjaji

et al. 2022

Concurrency and Computation

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This article puts forward a fast chaos-RSA-based hybrid cryptosystem to secure and authenticate secret images. The SHA-512 is used to generate a 512-bit initial key.The RSA system is used to encrypt the initial secret key and signature generation for both the sender and image authentication. In fact, a powerful block-cipher algorithm is developed to encrypt and decrypt images with a high level of security. At this stage, a strong PRNG based on four chaotic systems is propounded to generate high-quality keys. Therefore, an improved architecture is suggested. It performs confusion and diffusion of images with low computational complexity. In the final step, the encrypted secret key, signature, and encrypted image are combined together in order to obtain an encrypted signed image. The block-cipher algorithm is evaluated in-depth for several ordinary and medical images with different types, content, and size. The obtained simulation results demonstrate that the system enables high-level security. The entropy has achieved a value of 7.9998 which is the most important feature of randomness. A comparative study against numerous recent encryption algorithms demonstrates that the proposed algorithm provides good results.

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Chua's diode and strange attractor: a three‐layer hardware–software co‐design for medical image confidentiality

Cited by 38 publications

References 43 publications

Medical Image Transmission Using Novel Crypto-Compression Scheme

Medical Image Transmission Using Novel Crypto-Compression Scheme

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model

Improved chaos‐RSA‐based hybrid cryptosystem for image encryption and authentication

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