Towards a Universal Privacy Model for Electronic Health Record Systems: An Ontology and Machine Learning Approach

Nowrozy, Raza; Ahmed, Khandakar; Wang, Hua; McIntosh, Timothy R.

doi:10.3390/informatics10030060

Cited by 9 publications

(2 citation statements)

References 76 publications

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“…The ethical and privacy implications of behavioral analytics in RBAC systems warrant further investigation [48]. While behavioral analytics can significantly enhance insider threat detection, it raises concerns about privacy and potential misuse [49].…”

Section: Recentmentioning

confidence: 99%

From Theory to Practice: Implementing Effective Role-Based Access Control Strategies to Mitigate Insider Risks in Diverse Organizational Contexts

Marquis

2024

JERR

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This study investigates the effectiveness of Role-Based Access Control (RBAC) systems in mitigating insider threats to database security within various organizational environments. Insider threats represent a significant challenge for database security, necessitating robust and adaptive security measures. By delineating access based on users' roles within an organization, RBAC emerges as a critical tool against such threats. Employing a quantitative research methodology, this work gathered data through a survey targeting professionals directly involved in the security and management of organizational databases across technology, finance, healthcare, and government industries. The study utilized Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to validate the measurement model and analyze the relationships between RBAC effectiveness, implementation challenges, RBAC enhancements, and their collective impact on insider threat reduction. Findings indicate that RBAC effectively reduces unauthorized access and data breaches, significantly mitigating insider threats. However, implementation challenges such as role definition complexity and adapting to dynamic access needs emerge as notable obstacles. Enhancements in RBAC, mainly through integrating technologies like machine learning and dynamic access controls, are identified as critical mediators that enhance RBAC's effectiveness. The study concludes that while RBAC is a vital tool for database security, its success depends on continuous improvement and customization to specific organizational contexts. It recommends developing continuous enhancement programs for RBAC systems, specialized training for administrators, and the customization of RBAC strategies to meet unique organizational and industry needs. These measures are crucial for optimizing RBAC's effectiveness against insider threats.

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Section: Recentmentioning

confidence: 99%

From Theory to Practice: Implementing Effective Role-Based Access Control Strategies to Mitigate Insider Risks in Diverse Organizational Contexts

Marquis

2024

JERR

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“…Nowrozy R, et al [17] suggested an innovative Machine Learning (ML), based privacy framework for Electronic Health Records (EHR) systems in which a conceptual privacy ontology and ML techniques were deployed. This framework was employed for dealing with the issues occurred in HER systems such as to balance the privacy and availability, accessibility, and authentic compliance.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Model for Chronic Disease Prediction

Nowrozy*

2023

J Biomed Res Environ Sci

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Chronic diseases are a class of illnesses with complex etiologies, long-lasting or persistent symptoms, and are frequently referred to as chronic non communicable diseases. The most significant public health issues in the world are the leading chronic diseases, which include heart disease, cancer, chronic obstructive pulmonary disease, stroke, and diabetes. Both developed and developing nations experience significant health and financial costs as a result of these diseases. Some risk behaviours, such as tobacco use, poor nutrition, and excessive alcohol consumption, are the root cause of many chronic diseases. The various schemes are designed for the chronic disease prediction. The machine learning models for the chronic disease prediction is analysed in terms of certain parameters.

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Enhancing Health Information Systems Security: An Ontology Model Approach

Nowrozy,

Ahmed

2023

Lecture Notes in Computer Science

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Towards a Universal Privacy Model for Electronic Health Record Systems: An Ontology and Machine Learning Approach

Cited by 9 publications

References 76 publications

From Theory to Practice: Implementing Effective Role-Based Access Control Strategies to Mitigate Insider Risks in Diverse Organizational Contexts

From Theory to Practice: Implementing Effective Role-Based Access Control Strategies to Mitigate Insider Risks in Diverse Organizational Contexts

Machine Learning Model for Chronic Disease Prediction

Enhancing Health Information Systems Security: An Ontology Model Approach

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