Abstract
Recognising that the codes uncovered during a Grounded Theory analysis of semi-structured interview data can be interpreted as policy attributes, this paper describes how a Qualitative Research-based methodology can be extended to elicit Attribute Based Access Control style policies. In this methodology, user-participants are interviewed, and machine-learning is used to build a Bayesian Network based policy from the subsequent (Grounded Theory) analysis of the interview data.
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