A Security Management Framework for Big Data in Smart Healthcare

Sarosh, Parsa; Parah, Shabir A.; Bhat, G. Mohiuddin; Muhammad, Khan

doi:10.1016/j.bdr.2021.100225

Cited by 53 publications

(19 citation statements)

References 52 publications

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“…Sarosh P's research focused on the application of IoT big data management models in the field of medical big data analysis. He proposed an IoT big data management framework for medical big data [ 3 ]. Dong J's research is about the application of IoT big data management model in urban road network road maintenance.…”

Section: Related Workmentioning

confidence: 99%

Construction of a Public Service Cloud Platform for Disabled People Based on the Big Data Management Model of the Internet of Things

Liu

Chang

2022

Computational Intelligence and Neuroscience

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With the continuous development of social economy and the continuous improvement of people’s ideological level, the state and social people have given more and more care and assistance to the disadvantaged group of disabled persons. The construction of a public service cloud platform for the disabled is one of the important measures to protect the human rights of the disabled and improve the lives of the disabled. According to the statistics of the China Disabled Persons’ Federation, since the twentieth century, with the acceleration of the equalization of basic public services and the establishment of urban and rural social security systems, a practical and effective public service system for the disabled has made leaps and bounds. The construction of the cloud platform for public services for the disabled is an important branch of the construction of the public service system for the disabled. However, due to the late development of the construction of the cloud platform for public services for the disabled in China, it is not perfect, and there are still some problems to be solved. This paper mainly studies the public service cloud platform for the disabled based on the big data management model of the Internet of Things. Based on this research theme, this paper conducts a service response efficiency test for the disabled provided by the disabled public service cloud platform based on the big data management model of the Internet of Things. It concluded that the public service cloud platform for the disabled based on the big data management model of the Internet of Things has improved the response efficiency of providing services for the disabled by 57%.

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Section: Related Workmentioning

confidence: 99%

Construction of a Public Service Cloud Platform for Disabled People Based on the Big Data Management Model of the Internet of Things

Liu

Chang

2022

Computational Intelligence and Neuroscience

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“…Most of these systems are set for patients suffering from dementia [ 94 ] or diabetes [ 95 ] and with vascular pathologies [ 96 ].…”

Section: Resultsmentioning

confidence: 99%

Towards the Use of Big Data in Healthcare: A Literature Review

et al. 2022

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The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010–2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills.

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“…Many hybrid schemes that integrate the DNA and chaos-construct schemes have been recently proposed [16]. DNA computing offers minimum power consumption, parallelism, and high storage capacity and is incorporated in many encryption schemes to increase robustness [27].…”

Section: Introductionmentioning

confidence: 99%

An efficient image encryption scheme for healthcare applications

Sarosh

Parah

Bhat³

2022

Multimed Tools Appl

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In recent years, there has been an enormous demand for the security of image multimedia in healthcare organizations. Many schemes have been developed for the security preservation of data in e-health systems however the schemes are not adaptive and cannot resist chosen and known-plaintext attacks. In this contribution, we present an adaptive framework aimed at preserving the security and confidentiality of images transmitted through an e-healthcare system. Our scheme utilizes the 3D-chaotic system to generate a keystream which is used to perform 8-bit and 2-bit permutations of the image. We perform pixel diffusion by a key-image generated using the Piecewise Linear Chaotic Map (PWLCM). We calculate an image parameter using the pixels of the image and perform criss-cross diffusion to enhance security. We evaluate the scheme’s performance in terms of histogram analysis, information entropy analysis, statistical analysis, and differential analysis. Using the scheme, we obtain the average Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) values for an image of size 256 × 256 equal to 99.5996 and 33.499 respectively. Furthermore, the average entropy is 7.9971 and the average Peak Signal to Noise Ratio (PSNR) is 7.4756. We further test the scheme on 50 chest X-Ray images of patients having COVID-19 and viral pneumonia and found the average values of variance, PSNR, entropy, and Structural Similarity Index (SSIM) to be 257.6268, 7.7389, 7.9971, and 0.0089 respectively. Furthermore, the scheme generates completely uniform histograms for medical images which reveals that the scheme can resist statistical attacks and can be applied as a security framework in AI-based healthcare.

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A Security Management Framework for Big Data in Smart Healthcare

Cited by 53 publications

References 52 publications

Construction of a Public Service Cloud Platform for Disabled People Based on the Big Data Management Model of the Internet of Things

Construction of a Public Service Cloud Platform for Disabled People Based on the Big Data Management Model of the Internet of Things

Towards the Use of Big Data in Healthcare: A Literature Review

An efficient image encryption scheme for healthcare applications

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