An efficient Intra-Inter pixel encryption scheme to secure healthcare images for an IoT environment

Dash, Sachikanta; Padhy, Sasmita; Devi, S Anjali; Savyasachi,; Patro, K. Abhimanyu Kumar

doi:10.1016/j.eswa.2023.120622

Cited by 13 publications

(2 citation statements)

References 59 publications

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“…Undersampling and oversampling are the two predominant techniques [35]. Generally, oversampling methods tend to be more effective than undersampling techniques [36,37]. SMOTE is a widely recognized method for oversampling.…”

Section: B Data Pre-processingmentioning

confidence: 99%

Optimized Deep Belief Networks Based Categorization of Type 2 Diabetes using Tabu Search Optimization

Panigrahy,

Dash,

Padhy

2024

IJACSA

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Diabetics mellitus has the potential to result in numerous challenges. Based on the increasing morbidity rates in recent years, it is projected that the global diabetic population will surpass 642 million by 2040, indicating that approximately one in every ten individuals will have diabetes. Undoubtedly, this alarming statistic necessitates urgent focus from both academics as well as industry to foster novelty and advancement in prediction of diabetics, with the aim of preserving patients' lives. Deep learning (DL) was employed to forecast a multitude of ailments as a result of its swift advancement. Nevertheless, DL approaches continue to face challenges in achieving optimal prediction performance as a result of the selection of hyperparameters and tuning of parameters. Hence, the careful choice of hyper-parameters plays a crucial role in enhancing classification performance. This paper introduces TSO-DBN, a Tabu Search Optimization method (TSO) that is based on Deep Belief Network (DBN). TSO-DBN has demonstrated exceptional performance in several medical fields. The Tabu Search Optimization algorithm (TSO) has been used to pick hyperparameters and optimize parameters. During the experiment, two problems were tackled in order to improve the findings. The TSO-DBN model exhibited exceptional performance, surpassing other models with an accuracy of 96.23%, an F1-score of 0.8749, and a Matthews Correlation Coefficient (MCC) of 0.88.63.

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Section: B Data Pre-processingmentioning

confidence: 99%

Optimized Deep Belief Networks Based Categorization of Type 2 Diabetes using Tabu Search Optimization

Panigrahy,

Dash,

Padhy

2024

IJACSA

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“…The inherent characteristics of images, such as tight correlation, high redundancy, and block data capacity between adjacent pixels, distinguish image encryption from text encryption. Encryption is the process of hiding secret information by converting it into an unrecognizable form [1][2][3][4][5]. Therefore, network information security has become an important area of scientific research.…”

Section: Introductionmentioning

confidence: 99%

A Color Image-Encryption Algorithm Using Extended DNA Coding and Zig-Zag Transform Based on a Fractional-Order Laser System

Meng,

2023

Fractal Fract

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With the advancement of information technology, the security of digital images has become increasingly important. To ensure the integrity of images, a novel color image-encryption algorithm based on extended DNA coding, Zig-Zag transform, and a fractional-order laser system is proposed in this paper. First, the dynamic characteristics of the fractional-order laser chaotic system (FLCS) were analyzed using a phase diagram and Lyapunov exponent spectra. The chaotic sequences generated by the system were used to design image-encryption algorithms. Second, a modified Zig-Zag confusing method was adopted to confuse the image. Finally, in the diffusion link, the DNA encoding scheme was extended to allow for a greater number of DNA encoding rules, increasing the randomness of the matrix and improving the security of the encryption scheme. The performance of the designed encryption algorithm is analyzed using key space, a histogram, information entropy, correlation coefficients, differential attack, and robustness analysis. The experimental results demonstrate that the algorithm can withstand multiple decryption methods and has strong encryption capability. The proposed novel color image-encryption scheme enables secure communication of digital images.

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A chaotic based image encryption scheme using elliptic curve cryptography and genetic algorithm

Kumar,

Sharma

2024

Artif Intell Rev

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In the era of digital communication and data security, image encryption plays a crucial role in safeguarding sensitive information. Protecting sensitive visual data from unauthorized access drives the pursuit of advanced image encryption methods. This paper proposes a novel approach to enhance image encryption by combining the power of a chaotic map, elliptic curve cryptography, and genetic algorithm. The chaotic map, specifically Arnold’s cat map, is employed to introduce chaos and randomness into the encryption process. The proposed image encryption process involves applying Arnold’s cat map for shuffling the pixel positions, followed by elliptic curve cryptography for encrypting the pixel values using public and private keys. Additionally, a genetic algorithm is employed to optimize the key generation process, enhancing the security of the encryption scheme. The combined utilization of these techniques aims to achieve a high level of confidentiality and robustness in image encryption. The algorithm underwent thorough analysis. It achieved a maximum entropy score of 7.99, indicating a high level of randomness and unpredictability in the encrypted data. Additionally, it exhibited near-zero correlation, which suggests strong resistance against statistical attacks. Moreover, the cryptographic range of possible keys was found to be $$2^{511}$$ 2 511 . This extensive key space makes the algorithm highly resilient against brute force attacks. It took only 0.5634 s to encrypt a moderately sized $$512\times 512$$ 512 × 512 pixel image with an 8-bit image on a standard desktop computer with a 2.3 GHz processor and 16 GB of RAM. The experimental findings confirm that the proposed approach is highly effective and efficient in safeguarding sensitive image data from unauthorized access and potential attacks. This scheme has the benefit of allowing us to protect our private image data while it’s being transmitted.

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An efficient Intra-Inter pixel encryption scheme to secure healthcare images for an IoT environment

Cited by 13 publications

References 59 publications

Optimized Deep Belief Networks Based Categorization of Type 2 Diabetes using Tabu Search Optimization

Optimized Deep Belief Networks Based Categorization of Type 2 Diabetes using Tabu Search Optimization

A Color Image-Encryption Algorithm Using Extended DNA Coding and Zig-Zag Transform Based on a Fractional-Order Laser System

A chaotic based image encryption scheme using elliptic curve cryptography and genetic algorithm

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