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In this paper, the authors consider how qualitative research techniques that are used in applied psychology to understand a person’s feelings and needs provides a means to elicit their security needs.

Recognizing that the codes uncovered during a grounded theory analysis of semi-structured interview data can be interpreted as policy attributes, the paper develops a grounded theory-based methodology that 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.

Using a running example – based on a social psychology research study centered around photograph sharing – the paper demonstrates that in principle, qualitative research techniques can be used in a systematic manner to elicit security policy requirements.

While in principle qualitative research techniques can be used to elicit user requirements, the originality of this paper is a systematic methodology and its mapping into what is actionable, that is, providing a means to generate a machine-interpretable security policy at the end of the elicitation process.

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