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Role based access control design using three-way formal concept analysis
Published in Springer Science and Business Media LLC
2018
Volume: 9
   
Issue: 11
Pages: 1807 - 1837
Abstract

Role based access control (RBAC) is one of the popular access control models. On representing the policy behind RBAC, the literatures investigate the use of various knowledge representation techniques such as Descriptive logics, Formal Concept Analysis (FCA), Ontology etc. Based on the input of binary access control table, the existing knowledge representation techniques on RBAC derives two-way decisions whether to permit the access request or not. It works well when single element in the set of elements of a constituent of RBAC initiates the access request. Consider the scenario of multiple distinct elements in the set of elements of a constituent of RBAC initiate the collective access request to a set of elements in other constituent of RBAC. In many cases of this scenario, some elements possess but not all of the elements possess the permission to access all elements in other subset of a constituent of RBAC. On this situation, the collective access decision to those multiple distinct elements in the set of elements of a RBAC constituent appears in three forms such as permit, deny and non-commitment. Three-way formal concept analysis (3WCA) is an emerging knowledge representation technique which provides two types of three-way concepts and their lattices to enable three-way decisions from the binary information table. At this juncture, it is more suitable to apply 3WCA on representing the RBAC policy to enable three-way decisions instead of existing two-way decisions in classical FCA and triadic FCA. The main objective of this paper is to propose a methodology for modelling RBAC using 3WCA and attain its distinctive merits. Our discussion is on two lines of inquiry. We present on how 3WCA can provide suitable representation of RBAC policy and whether this representation follows role hierarchy and constraints of RBAC.

About the journal
JournalData powered by TypesetInternational Journal of Machine Learning and Cybernetics
PublisherData powered by TypesetSpringer Science and Business Media LLC
ISSN1868-8071
Open Access0