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What Users Want: Adapting Qualitative Research Methods to Security Policy Elicitation

机译:用户想要的:适应安全政策诱因的定性研究方法

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摘要

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.
机译:认识到在半结构访谈数据的接地理论分析期间发现的代码可以被解释为策略属性,本文介绍了如何扩展了定性的基于研究的方法,以引发基于属性的访问控制样式策略。在这种方法中,用户参与者接受了采访,并且机器学习用于从随后的(接地的理论)分析面试数据的基于贝叶斯网络的基础政策。

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