Based on IoT Healthcare Application for Medical Data Authentication: Towards A New Secure Framework Using Steganography

Hashim, Mohammad; Rhaif, Suhad Hasan; Abdulrazzaq, Ali Abdulwahhab; Ali, Adnan Hussein; Taha, Mustafa Sabah

doi:10.1088/1757-899x/881/1/012120

Cited by 18 publications

(8 citation statements)

References 32 publications

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“…4b). These papers predominantly spanned the medical domain (n=3, [23,29,48]) and a newly emerging financial domain (n=2, [53,63]).…”

Section: Conference Papersmentioning

confidence: 99%

Evaluating the Merits and Constraints of Cryptography-Steganography Fusion: A Systematic Analysis

Haverkamp,

Sarmah

2023

Preprint

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In an interconnected world, ensuring the confidentiality and security of digital data has become increasingly vital. Cryptography and steganography serve as two primary approaches to information security. Cryptography shields the content of messages, while steganography conceals their existence. While these methods find applications in diverse fields, there is ongoing exploration regarding the potential benefits of combining them. However, the practicality of merging these techniques hinges upon various factors, including bandwidth limitations, latency constraints, and specific security requirements pertinent to a given scenario, such as electronic voting. This review focuses on journal articles and conference papers implementing the integration of steganography and cryptography in practical applications. Notably, the majority of research conducted so far has been limited to medical applications, whereas image steganography finds widespread use across diverse domains. In this novel research, we do the extensive review in the following ways: i) By gaining valuable insights into the real-world applications being explored in the existing literature., and ii) By categorizing these applications based on their domain of applications (e.g., Medical or Transportation) and technological domain (e.g., Cloud Computing or Internet of Things). Moreover, this review thoroughly analyses the advantages and limitations associated with these implementations and discusses them from three evaluation perspectives: security, performance, and user experience. The categorization of applications facilitates guidance for future research in unexplored areas, while the three evaluation perspectives provide crucial aspects to consider when analyzing or evaluating real-world implementations.

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“…4b). These papers predominantly spanned the medical domain (n=3, [23,29,48]) and a newly emerging financial domain (n=2, [53,63]).…”

Section: Conference Papersmentioning

confidence: 99%

Evaluating the Merits and Constraints of Cryptography-Steganography Fusion: A Systematic Analysis

Haverkamp,

Sarmah

2023

Preprint

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“…This type of credential is straightforward and does not need a technique for extracting its value. This authentication scheme facilitates cryptography, steganography, or shadow to hide such credentials [42,[62][63][64][65][66][67]. F. Wu et al [68] facilitated a dynamic pseudo-identity and a collision-resistant cryptographic function to hide users' identities.…”

Section: Authentication Credentialsmentioning

confidence: 99%

Authentication in the Internet of Medical Things: Taxonomy, Review, and Open Issues

Alsaeed

Nadeem

2022

Applied Sciences

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The Internet of Medical Things (IoMT) has revolutionized the world of healthcare by remotely connecting patients to healthcare providers through medical devices connected over the Internet. IoMT devices collect patients’ medical data and share them with healthcare providers, who analyze it for early control of diseases. The security of patients’ data is of prime importance in IoMT. Authentication of users and devices is the first layer of security in IoMT. However, because of diverse and resource-constrained devices, authentication in IoMT is a challenging task. Several authentication schemes for IoMT have been proposed in the literature. However, each of them has its own pros and cons. To identify, evaluate and summarize the current literature on authentication in IoMT, we conducted a systematic review of 118 articles published between 2016 and 2021. We also established a taxonomy of authentication schemes in IoMT from seven different perspectives. We observed that most of the authentication schemes use a distributed architecture and public key infrastructure. It was also observed that hybrid cryptography approaches have become popular to overcome the shortcomings of single cryptographic approaches. Authentication schemes in IoMT need to support end-to-end, cross-layer, and cross-domain authentication. Finally, we discuss some open issues and future directions.

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“…This method permits the stratification of patients into risk categories, offers prognostic estimations, and improves medical expert decision-making. Pulmonary hypertension and pneumonia are the most prevalent CT findings linked with clinical severity [11], [12]. On CT scans, symptoms of severe COVID-19 are ground glassy opacity (GGO), as well as pulmonary infiltrates (PIs) in a periphery pleural region; these symptoms often emerge within 14 days of virus exposure [13].…”

Section: Introductionmentioning

confidence: 99%

Based on deep convolutional neural network, COVID-19 identification utilizing computed tomography scans

Yonan,

Fadheel,

Al-Doori

et al. 2024

Bulletin EEI

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In the year 2019 specifically, on March 11th, the coronavirus illness two thousand nineteen (COVID-19) was announced a worldwide epidemic due to its rapid spread and lack of treatment options. As a result, infected individuals must be identified and quarantined quickly to prevent the illness from spreading. The method used to test for COVID-19 is called real-time-polymerase chain reaction (RT-PCR), which has problems with having low sensitivity and taking an extended amount of time. Because chest computed tomography (CT) scans are more sensitive than RT-PCR, it follows that such scans can be employed for diagnostic purposes. This study developed a deep convolutional neural network (CNN) approach to detect COVID-19 using CT scan images. An architecture of deep learning (DL) called convolutional neural network computed tomography scans (CT-CNN) was utilized to efficiently identify COVID-19. The findings of our suggested model are highly encouraging, with an accuracy of 96.14%, an F1 score of 96.21%, and a recall of 97.53% when it comes to classifying CT scans as either infected or not infected by COVID-19.

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Based on IoT Healthcare Application for Medical Data Authentication: Towards A New Secure Framework Using Steganography

Cited by 18 publications

References 32 publications

Evaluating the Merits and Constraints of Cryptography-Steganography Fusion: A Systematic Analysis

Evaluating the Merits and Constraints of Cryptography-Steganography Fusion: A Systematic Analysis

Authentication in the Internet of Medical Things: Taxonomy, Review, and Open Issues

Based on deep convolutional neural network, COVID-19 identification utilizing computed tomography scans

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