A fine-grained context-aware access control model for health care and life science linked data

Health Care and Life Sciences (HCLS) have long been a test-bed for the standards proposed by the W3C to build the Semantic Web. One of the challenges to HCLS Linked Data is access control. In this paper, we present a fine-grained context-aware access model for HCLS Linked Data based on Semantic Web...

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Published inMultimedia tools and applications Vol. 75; no. 22; pp. 14263 - 14280
Main Authors Liu, Zhengtao, Wang, Jiandong
Format Journal Article
LanguageEnglish
Published New York Springer US 01.11.2016
Springer Nature B.V
Subjects
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ISSN1380-7501
1573-7721
DOI10.1007/s11042-016-3269-6

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Abstract Health Care and Life Sciences (HCLS) have long been a test-bed for the standards proposed by the W3C to build the Semantic Web. One of the challenges to HCLS Linked Data is access control. In this paper, we present a fine-grained context-aware access model for HCLS Linked Data based on Semantic Web tools. The model consists of two basic components: ontology base and access policy. Ontology base refers to a set of ontologies that include subject ontology, resource ontology, environment ontology, and action ontology. In the access policy module, we describe the access policy with eXtensible Access Control Markup Language (XACML) model, which allows users to achieve access rule reproduction by defining the semantic scope and inference rules among different entities. Results of the analysis indicate that indicates that our model expands the scopes of authorization rules for users. Inference of semantic authorization rules is also realized. These rules enable fine-grained access to data and meet the need for dynamic change of HCLS Linked Data. Finally, we show the process of authorization and present a system framework. Simulation experiments verify the acceptability of our model in protecting secured data.
AbstractList Health Care and Life Sciences (HCLS) have long been a test-bed for the standards proposed by the W3C to build the Semantic Web. One of the challenges to HCLS Linked Data is access control. In this paper, we present a fine-grained context-aware access model for HCLS Linked Data based on Semantic Web tools. The model consists of two basic components: ontology base and access policy. Ontology base refers to a set of ontologies that include subject ontology, resource ontology, environment ontology, and action ontology. In the access policy module, we describe the access policy with eXtensible Access Control Markup Language (XACML) model, which allows users to achieve access rule reproduction by defining the semantic scope and inference rules among different entities. Results of the analysis indicate that indicates that our model expands the scopes of authorization rules for users. Inference of semantic authorization rules is also realized. These rules enable fine-grained access to data and meet the need for dynamic change of HCLS Linked Data. Finally, we show the process of authorization and present a system framework. Simulation experiments verify the acceptability of our model in protecting secured data.
Author Wang, Jiandong
Liu, Zhengtao
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CitedBy_id crossref_primary_10_1002_dac_4554
crossref_primary_10_3233_SW_223088
crossref_primary_10_1016_j_sysarc_2022_102625
Cites_doi 10.3390/s150820925
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Health care and life science linked data
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SubjectTerms Access control
Access to information
Analysis
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Datasets
Experiments
Extensible Markup Language
Health care
Inference
Knowledge representation
Life sciences
Linked Data
Mathematical models
Metadata
Multimedia Information Systems
Ontology
Policies
Security management
Semantic web
Semantics
Special Purpose and Application-Based Systems
Studies
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Title A fine-grained context-aware access control model for health care and life science linked data
URI https://link.springer.com/article/10.1007/s11042-016-3269-6
https://www.proquest.com/docview/1873931940
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Volume 75
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