Ontology-Based Approach for the Measurement of Privacy Disclosure

Zhu, Nafei; Chen, Baocun; Wang, Siyu; Teng, Da; He, Jingsha

doi:10.1007/s10796-021-10180-2

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…It focuses on representing policies related to patient consent, confidentiality, and access control. PolicyCore facilitates policy reasoning and enforcement in healthcare information systems [16].…”

Section: Policy Corementioning

confidence: 99%

Untitled

2023

J Curr Trends Comp Sci Res

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This literature review focuses on the development of an ontology for public policy problems in Ethiopia. The study aims to provide a structured representation of concepts, relationships, and knowledge specific to the Ethiopian policy context. The research follows a systematic ontology development process that incorporates domain-specific knowledge and stakeholder involvement.The review begins by describing the steps involved in the ontology development process, including problem identification and scoping, requirement analysis, ontology modeling, and stakeholder involvement. It emphasizes the importance of engaging policymakers, subject matter experts, and researchers throughout the process to validate the ontology and ensure its relevance and effectiveness in addressing Ethiopian policy problems.

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Section: Policy Corementioning

confidence: 99%

Untitled

2023

J Curr Trends Comp Sci Res

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“…Research on ontology-based approaches for protecting personal information in online privacy policies suggests that these models offer a standardized and objective way to measure privacy disclosure [67]. Privacy-preserving ontology is analyzed through various stages, including data collection, data publication, and output regarding modeling and training.…”

Section: Privacy Disclosure Measurementmentioning

confidence: 99%

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

et al. 2023

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This paper proposed a novel privacy model for Electronic Health Records (EHR) systems utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It underscores the challenges currently faced by EHR systems such as balancing privacy and accessibility, user-friendliness, and legal compliance. To address these challenges, the study developed a universal privacy model designed to efficiently manage and share patients’ personal and sensitive data across different platforms, such as MHR and NHS systems. The research employed various BERT techniques to differentiate between legitimate and illegitimate privacy policies. Among them, Distil BERT emerged as the most accurate, demonstrating the potential of our ML-based approach to effectively identify inadequate privacy policies. This paper outlines future research directions, emphasizing the need for comprehensive evaluations, testing in real-world case studies, the investigation of adaptive frameworks, ethical implications, and fostering stakeholder collaboration. This research offers a pioneering approach towards enhancing healthcare information privacy, providing an innovative foundation for future work in this field.

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Privacy Preservation Technique Based on Sensitivity Levels for Multiple Numerical Sensitive Overlapped Attributes

Chourey

Soni

2022

Lecture Notes in Networks and Systems

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Ontology-Based Approach for the Measurement of Privacy Disclosure

Cited by 5 publications

References 20 publications

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

Privacy Preservation Technique Based on Sensitivity Levels for Multiple Numerical Sensitive Overlapped Attributes

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