Cybersecurity and Data Privacy in the Cloudlet for Preliminary Healthcare Big Data Analytics

Javid, Tariq; Faris, Muhammad; Beenish, Hira; Fahad, Muhammad

doi:10.1109/iccit-144147971.2020.9213712

Cited by 9 publications

(9 citation statements)

References 21 publications

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“…Table 2 summarises the definitions that were found in our selected studies: Ref. [31] identifies six Industry 4.0 principles applied to the Healthcare sector: interoperability, visualisation, decentralisation, real‐time capability, service orientation and modularity. They enable the integration of the fourth industrial revolution in Healthcare, with cloud‐based design and manufacturing systems and the Industrial Internet of Things being the key enabling technologies. Ref.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, Ref. [31] deals with the concept of Cloudlets, which are virtual machines hosted close to the wireless devices, linked to Mobile Edge Computing. They can be used to collect and transfer medical data, while also performing some low‐level Data Analytics on it.…”

Section: Resultsmentioning

confidence: 99%

“…Some authors even claim that Industry 4.0 is mainly enabled by CPS [27]. They are defined as ‘engineered systems that are built from, and depend on, the seamless integration of computational algorithms and physical components’ [31] or ‘transformative technologies for managing interconnected systems between its physical assets and computational capabilities’ [85] and are characterised by the ability to perform computations and interact with the physical world [86]. Thus, they are a key feature for systems integration, both horizontal and vertical.…”

Section: Resultsmentioning

confidence: 99%

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Industry 4.0 and healthcare: Context, applications, benefits and challenges

et al. 2022

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Industry 4.0 refers to the digital transformation in the manufacturing domain through new technology. Currently, it expands well beyond manufacturing, affecting many areas of life and posing implications for all types of business. This paper focuses on the relationships between Industry 4.0 and Healthcare which transitions to increased interconnectivity, automation and smart decision making. The integration context of Industry 4.0 into Healthcare is only partly understood. Little was done until now to consolidate what is known on the integration benefits and the challenges. This article reports results of a systematic mapping study that analysed 69 papers to extract knowledge about the concepts of Industry 4.0 and the emerging Healthcare 4.0., and the relationships between them. We found 10 different perspectives of Healthcare 4.0, ranging from strategic to tactical and operational levels. Next, our results show: (i) nine applications of Industry 4.0 in the Healthcare domain: Augmented Reality and Simulation, Autonomous Robotics, Cybersecurity, Big Data Analytics, Internet of Things, Cloud Computing, Additive Manufacturing and Systems Integration; and (ii) 10 benefits and nine challenges in Healthcare 4.0. The most frequently mentioned benefits are patients' diagnosis, monitoring, treatment, and financial benefits. The most researched challenges are data fragmentation, heterogeneity, complexity, and privacy.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Industry 4.0 and healthcare: Context, applications, benefits and challenges

et al. 2022

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“…Javid et al [40] underlined the difficulties of cyber security and data privacy and developing solutions in adopting Industry 4.0 in the healthcare industry. For example, a reduction in the attack surface is required to seamlessly integrate complex computational algorithms, such as those used in cryptography.…”

Section: Data Confidentialitymentioning

confidence: 99%

Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values

Lee

Jeong

Jung

et al. 2022

JPM

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With the development of big data and cloud computing technologies, the importance of pseudonym information has grown. However, the tools for verifying whether the de-identification methodology is correctly applied to ensure data confidentiality and usability are insufficient. This paper proposes a verification of de-identification techniques for personal healthcare information by considering data confidentiality and usability. Data are generated and preprocessed by considering the actual statistical data, personal information datasets, and de-identification datasets based on medical data to represent the de-identification technique as a numeric dataset. Five tree-based regression models (i.e., decision tree, random forest, gradient boosting machine, extreme gradient boosting, and light gradient boosting machine) are constructed using the de-identification dataset to effectively discover nonlinear relationships between dependent and independent variables in numerical datasets. Then, the most effective model is selected from personal information data in which pseudonym processing is essential for data utilization. The Shapley additive explanation, an explainable artificial intelligence technique, is applied to the most effective model to establish pseudonym processing policies and machine learning to present a machine-learning process that selects an appropriate de-identification methodology.

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“…Intelligent wearable devices have been widely used in the healthcare environment to detect body information and monitor patients' health conditions [37]. According to [38], a cyber-physical system (CPS) is defined as "engineered systems that are developed from and rely on the tight integration of computer algorithms and physical elements" and serves as the foundation of the fourth industrial revolution. In addition, most linked systems, known as Cyber-Physical Systems (CPS), are built by combining several elements such as humans and the physical environment, intelligent objects, and embedded devices and infrastructure [39].…”

Section: Cyber Securitymentioning

confidence: 99%

The Effectiveness of IoT Based Wearable Devices and Potential Cybersecurity Risks: A Systematic Literature Review from the Last Decade

et al. 2022

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Wearable technology has enormous promise, particularly for data collection for cutting-edge health research, and its popularity has soared in recent years. This study aims to provide IoT-based wearable devices' effectiveness and potential cybersecurity threats to these innovative technologies. Using the PRISMA-2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, we conducted a scoping study to understand better the application of inexpensive, consumer-grade wearables for health research from a population health viewpoint. Scopus databases yielded a total of 43 articles. Our findings show that academics and research participants are tremendously interested in this technology, but they are also wary about using wearables. These devices are still vulnerable to cybersecurity assaults such as data privacy and security in healthcare. However, blockchain technology is a potential alternative for integrating with IoT-based wearables to ensure data privacy. Furthermore, according to the findings, wearables have an increasingly diverse range of applications, including COVID-19 prediction, fertility tracking, heat-related sickness, pharmacological effects, and psychological therapies; they also include underrepresented populations, such as those with rare diseases. In low-resource environments, there is a lack of research on wearable devices.

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Cybersecurity and Data Privacy in the Cloudlet for Preliminary Healthcare Big Data Analytics

Cited by 9 publications

References 21 publications

Industry 4.0 and healthcare: Context, applications, benefits and challenges

Industry 4.0 and healthcare: Context, applications, benefits and challenges

Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values

The Effectiveness of IoT Based Wearable Devices and Potential Cybersecurity Risks: A Systematic Literature Review from the Last Decade

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