Security and Privacy Requirements for Cloud Computing in Healthcare

Ermakova, Tatiana; Fabian, Benjamin; Kornacka, Marta; Thiebes, Scott; Sunyaev, Ali

doi:10.1145/3386160

Cited by 27 publications

(11 citation statements)

References 92 publications

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“…We have also found some works in the literature that report on the use of use cases, business process modeling, class diagrams, user story, user experience, design thinking, among others, for software privacy and privacy requirements' elicitation [44,48,49,[56][57][58] and have related works to software privacy and privacy requirements for cloud deployment models [46,47,59] and Internet of Things (IoT) [41,42,60,61]. This diversity of works found in several areas [43,45,62] demonstrates how important the elicitation of software privacy and privacy requirements is during the software development process and the growing concern of researchers from various fields of knowledge regarding the privacy of user data.…”

Section: Rq1 According To the Literature What Are The Methodologiementioning

confidence: 95%

Perceptions of ICT Practitioners Regarding Software Privacy

Canedo

Calazans

Masson

et al. 2020

Entropy

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During software development activities, it is important for Information and Communication Technology (ICT) practitioners to know and understand practices and guidelines regarding information privacy, as software requirements must comply with data privacy laws and members of development teams should know current legislation related to the protection of personal data. In order to gain a better understanding on how industry ICT practitioners perceive the practical relevance of software privacy and privacy requirements and how these professionals are implementing data privacy concepts, we conducted a survey with ICT practitioners from software development organizations to get an overview of how these professionals are implementing data privacy concepts during software design. We performed a systematic literature review to identify related works with software privacy and privacy requirements and what methodologies and techniques are used to specify them. In addition, we conducted a survey with ICT practitioners from different organizations. Findings revealed that ICT practitioners lack a comprehensive knowledge of software privacy and privacy requirements and the Brazilian General Data Protection Law (Lei Geral de Proteção de Dados Pessoais, LGPD, in Portuguese), nor they are able to work with the laws and guidelines governing data privacy. Organizations are demanded to define an approach to contextualize ICT practitioners with the importance of knowledge of software privacy and privacy requirements, as well as to address them during software development, since LGPD must change the way teams work, as a number of features and controls regarding consent, documentation, and privacy accountability will be required.

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Section: Rq1 According To the Literature What Are The Methodologiementioning

confidence: 95%

Perceptions of ICT Practitioners Regarding Software Privacy

Canedo

Calazans

Masson

et al. 2020

Entropy

View full text Add to dashboard Cite

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“…Step 7: stop ALGORITHM 3: Cloud Encryption Algorithm and Decryption Algorithm. 8 Computational Intelligence and Neuroscience of the proposed system is determined by using the Clinical Data Progression approach in which unique identification is required.…”

Section: F1 Score F1mentioning

confidence: 99%

“…An individual's trust in the safety and confidentiality of their medical history had a positive impact on their motivation to create an electronic health record [ 7 ]. Patients' ability to encourage health care providers to exchange their medical data by using cloud computing techniques has been [ 8 ] limited as a result of privacy issues. Antivirus tools, chief information security officers, and cloud computing are other security methods that are used, but their deployment is [ 9 ] budget-dependent.…”

Section: Introductionmentioning

confidence: 99%

Clinical Text Data Categorization and Feature Extraction Using Medical-Fissure Algorithm and Neg-Seq Algorithm

Pagad

Pradeep²,

Almuzaini

et al. 2022

Computational Intelligence and Neuroscience

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A large amount of patient information has been gathered in Electronic Health Records (EHRs) concerning their conditions. An EHR, as an unstructured text document, serves to maintain health by identifying, treating, and curing illnesses. In this research, the technical complexities in extracting the clinical text data are removed by using machine learning and natural language processing techniques, in which an unstructured clinical text data with low data quality is recognized by Halve Progression, which uses Medical-Fissure Algorithm which provides better data quality and makes diagnosis easier by using a cross-validation approach. Moreover, to enhance the accuracy in extracting and mapping clinical text data, Clinical Data Progression uses Neg-Seq Algorithm in which the redundancy in clinical text data is removed. Finally, the extracted clinical text data is stored in the cloud with a secret key to enhance security. The proposed technique improves the data quality and provides an efficient data extraction with high accuracy of 99.6%.

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“…Ermakova et al [19] surveyed the use of cloud computing technology in healthcare. The survey pointed out the importance, for GPs, to share healthcare data under a common standard system [20] – [22] .…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System for Diabetes Chronic Care Models Based on General Practitioner Engagement and EHR Data Sharing

Frontoni

Romeo

Bernardini

et al. 2020

IEEE J. Transl. Eng. Health Med.

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Objective Decision support systems (DSS) have been developed and promoted for their potential to improve quality of health care. However, there is a lack of common clinical strategy and a poor management of clinical resources and erroneous implementation of preventive medicine. Methods To overcome this problem, this work proposed an integrated system that relies on the creation and sharing of a database extracted from GPs’ Electronic Health Records (EHRs) within the Netmedica Italian (NMI) cloud infrastructure. Although the proposed system is a pilot application specifically tailored for improving the chronic Type 2 Diabetes (T2D) care it could be easily targeted to effectively manage different chronic-diseases. The proposed DSS is based on EHR structure used by GPs in their daily activities following the most updated guidelines in data protection and sharing. The DSS is equipped with a Machine Learning (ML) method for analyzing the shared EHRs and thus tackling the high variability of EHRs. A novel set of T2D care-quality indicators are used specifically to determine the economic incentives and the T2D features are presented as predictors of the proposed ML approach. Results The EHRs from 41237 T2D patients were analyzed. No additional data collection, with respect to the standard clinical practice, was required. The DSS exhibited competitive performance (up to an overall accuracy of 98%±2% and macro-recall of 96%±1%) for classifying chronic care quality across the different follow-up phases. The chronic care quality model brought to a significant increase (up to 12%) of the T2D patients without complications. For GPs who agreed to use the proposed system, there was an economic incentive. A further bonus was assigned when performance targets are achieved. Conclusions The quality care evaluation in a clinical use-case scenario demonstrated how the empowerment of the GPs through the use of the platform (integrating the proposed DSS), along with the economic incentives, may speed up the improvement of care.

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Security and Privacy Requirements for Cloud Computing in Healthcare

Cited by 27 publications

References 92 publications

Perceptions of ICT Practitioners Regarding Software Privacy

Perceptions of ICT Practitioners Regarding Software Privacy

Clinical Text Data Categorization and Feature Extraction Using Medical-Fissure Algorithm and Neg-Seq Algorithm

A Decision Support System for Diabetes Chronic Care Models Based on General Practitioner Engagement and EHR Data Sharing

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