Achieving data security and privacy across healthcare applications using cyber security mechanisms

Zhu, Shanying; Saravanan, Vijayalakshmi; Muthu, BalaAnand

doi:10.1108/el-07-2020-0219

Cited by 35 publications

(9 citation statements)

References 33 publications

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“…Each time when the data is stored in Data Lake, a pointer is provided with every health record. It is then submitted with blockchain combined with a unique user key (Zhu et al 2020). This process is repeated for each patient and gets updated with a digital signature to be accessibility each form.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A cognitive approach for blockchain-based cryptographic curve hash signature (BC-CCHS) technique to secure healthcare data in Data Lake

Panwar

Bhatnagar

2021

Soft Comput

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A cognitive approach for blockchain-based cryptographic curve hash signature (BC-CCHS) technique to secure healthcare data in Data Lake

Panwar

Bhatnagar

2021

Soft Comput

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“…By using this method, data is shared with the data-miners without compromising individual's privacy while knowledge is preserved in the published data [93]. The other notable solutions that have demonstrated effectiveness in terms of privacy in data publishing are, pseudonymization [94], privacy preservation leveraging machine learning models [95], data driven anonymization system [96,97], cyber security mechanisms [98], and non-cryptographic anonymization techniques [99]. All these solutions have demonstrated effectiveness for preserving people's privacy when data is published for research/innovation purposes.…”

Section: Latest Technologies/solutions Devised So For To Alleviate Prmentioning

confidence: 99%

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Majeed

Lee

2021

Inventions

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With the advent of the pandemic (e.g., novel corona virus disease 2019 (COVID-19)), a tremendous amount of data about individuals are collected by the health authorities on daily basis for curbing the disease’s spread. The individuals’ data collection/processing at a massive scale for community well-being with the help of digital solutions (e.g., mobile apps for mobility and proximity analysis, contact tracing through credit card usage history, facial recognition through cameras, and crowd analysis using cellular networks data etc.) raise several privacy concerns. Furthermore, the privacy concerns that are arising mainly due to the fine-grained data collection has hindered the response to tackle this pandemic in many countries. Hence, acquiring/handling individuals data with privacy protection has become a vibrant area of research in these pandemic times. This paper explains the shift in privacy paradigm due to the pandemic (e.g., COVID-19) which involves more and detailed data collection about individuals including locations and demographics. We explain technical factors due to which the people’s privacy is at higher risk in the COVID-19 time. In addition, we discuss privacy concerns in different epidemic control measures (ECMs) (e.g., contact tracing, quarantine monitoring, and symptoms reporting etc.) employed by the health authorities to tackle this disease. Further, we provide an insight on the data management in the ECMs with privacy protection. Finally, the future prospects of the research in this area tacking into account the emerging technologies are discussed. Through this brief article, we aim to provide insights about the vulnerability to user’s privacy in pandemic times, likely privacy issues in different ECMs adopted by most countries around the world, how to preserve user’s privacy effectively in all phases of the ECMs considering relevant data in loop, and conceptual foundations of ECMs to fight with future pandemics in a privacy preserving manner.

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“…If the scientific data is stored improperly, some unique data may be permanently destroyed. Scientific data preserving should consider various aspects, such as data naming rules, the preserving location, the data storage format, the backup strategy and data security, and so on (Zhu et al , 2020).…”

Section: A General Life Cycle Model For Scientific Datamentioning

confidence: 99%

Process-driven quality improvement for scientific data based on information product map

Zong

Song-tao

Gao

et al. 2022

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Purpose This paper aims to provide a process-driven scientific data quality (DQ) monitoring framework by information product map (IP-Map) in identifying the root causes of poor DQ issues so as to assure the quality of scientific data. Design/methodology/approach First, a general scientific data life cycle model is constructed based on eight classical models and 37 researchers’ experience. Then, the IP-Map is constructed to visualize the scientific data manufacturing process. After that, the potential deficiencies that may arise and DQ issues are examined from the aspects of process and data stakeholders. Finally, the corresponding strategies for improving scientific DQ are put forward. Findings The scientific data manufacturing process and data stakeholders’ responsibilities could be clearly visualized by the IP-Map. The proposed process-driven framework is helpful in clarifying the root causes of DQ vulnerabilities in scientific data. Research limitations/implications As for the implications for researchers, the process-driven framework proposed in this paper provides a better understanding of scientific DQ issues during implementing a research project as well as providing a useful method to analyse those DQ issues based on IP-Map approach from the aspects of process and data stakeholders. Practical implications The process-driven framework is beneficial for the research institutions, scientific data management centres and researchers to better manage the scientific data manufacturing process and solve the scientific DQ issues. Originality/value This research proposes a general scientific data life cycle model and further provides a process-driven scientific DQ monitoring framework for identifying the root causes of poor data issues from the aspects of process and stakeholders which have been ignored by existing information technology-driven solutions. This study is likely to lead to an improved approach to assuring the scientific DQ and is applicable in different research fields.

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Achieving data security and privacy across healthcare applications using cyber security mechanisms

Cited by 35 publications

References 33 publications

RETRACTED ARTICLE: A cognitive approach for blockchain-based cryptographic curve hash signature (BC-CCHS) technique to secure healthcare data in Data Lake

RETRACTED ARTICLE: A cognitive approach for blockchain-based cryptographic curve hash signature (BC-CCHS) technique to secure healthcare data in Data Lake

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Process-driven quality improvement for scientific data based on information product map

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