Context-Aware Access Control with Imprecise Context Characterization Through a Combined Fuzzy Logic and Ontology-Based Approach

  • A. S. M. Kayes
  • Wenny Rahayu
  • Tharam Dillon
  • Elizabeth Chang
  • Jun Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10573)


Context information plays a crucial role in dynamically changing environments and the different types of contextual conditions bring new challenges to access control. This information mostly can be derived from the crisp sets. For example, we can utilize a crisp set to derive a patient and nurse are co-located in the general ward of the hospital or not. Some of the context information characterizations cannot be made using crisp sets, however, they are equally important in order to make access control decisions. For example, a patient’s current health status is “critical” or “high critical” which are imprecise fuzzy facts, whereas “95% level of maximum blood pressure allowed” is precise. Thus, there is a growing need for integrating these kinds of fuzzy and other conditions to appropriately control context-specific access to information resources at different granularity levels. Towards this goal, this paper introduces an approach to Context-Aware Access Control using Fuzzy logic (FCAAC) for information resources. It includes a formal context model to represent the fuzzy and other contextual conditions. It also includes a formal policy model to specify the policies by utilizing these conditions. Using our formal approach, we combine the fuzzy model with an ontology-based approach that captures such contextual conditions and incorporates them into the policies, utilizing the ontology languages and the fuzzy logic-based reasoning. We justify the feasibility of our approach by demonstrating the practicality through a prototype implementation and a healthcare case study, and also evaluating the performance in terms of response time.


Context-aware access control Fuzzy facts Contextual conditions Context model Fuzzy reasoning model Policy model 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • A. S. M. Kayes
    • 1
  • Wenny Rahayu
    • 1
  • Tharam Dillon
    • 1
  • Elizabeth Chang
    • 2
  • Jun Han
    • 3
  1. 1.La Trobe UniversityMelbourneAustralia
  2. 2.University of New South WalesCanberraAustralia
  3. 3.Swinburne University of TechnologyMelbourneAustralia

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