Proceedings of the 13th Symposium on Access control Models and Technologies

ROWLBAC - Representing Role Based Access Control in OWL

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There have been two parallel themes in access control research in recent years. On the one hand there are efforts to develop new access control models to meet the policy needs of real world application domains. In parallel, and almost separately, researchers have developed policy languages for access control. This paper is motivated by the consideration that these two parallel efforts need to develop synergy. A policy language in the abstract without ties to a model gives the designer little guidance. Conversely a model may not have the machinery to express all the policy details of a given system or may deliberately leave important aspects unspecified. Our vision for the future is a world where advanced access control concepts are embodied in models that are supported by policy languages in a natural intuitive manner, while allowing for details beyond the models to be further specified in the policy language.

This paper studies the relationship between the Web Ontology Language (OWL) and the Role Based Access Control (RBAC) model. Although OWL is a web ontology language and not specifically designed for expressing authorization policies, it has been used successfully for this purpose in previous work. OWL is a leading specification language for the Semantic Web, making it a natural vehicle for providing access control in that context. In this paper we show two different ways to support the NIST Standard RBAC model in OWL and then discuss how the OWL constructions can be extended to model attribute-based RBAC or more generally attribute-based access control. We further examine and assess OWL's suitability for two other access control problems: supporting attribute based access control and performing security analysis in a trust-management framework.

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access control, owl, policy, rbac, security, semantic web


ACM Press

Estes Park, Colorado, USA

awarded the SACMAT 10-year best paper award in 2018

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