Evaluating role mining algorithms

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Evaluating role mining algorithms
While many role mining algorithms have been proposed in recent years, there lacks a comprehensive study to compare these algorithms. These role mining algorithms have been evaluated when they were proposed, but the evaluations were using different datasets and evaluation criteria. In this paper, we introduce a comprehensive framework for evaluating role mining algorithms. We categorize role mining algorithms into two classes based on their outputs; Class 1 algorithms output a sequence of prioritized roles while Class 2 algorithms output complete RBAC states. We then develop techniques that enable us to compare these algorithms directly. We also introduce a new role mining algorithm and two new ways for algorithmically generating datasets for evaluation. Using synthetic as well as real datasets, we compared nine role mining algorithms. Our results illustrate the strengths and weaknesses of these algorithms. Categories and Subject Descriptors D.4.6 [Operating Systems]: Security and Pro...
Ian Molloy, Ninghui Li, Tiancheng Li, Ziqing Mao,
Added 28 May 2010
Updated 28 May 2010
Type Conference
Year 2009
Authors Ian Molloy, Ninghui Li, Tiancheng Li, Ziqing Mao, Qihua Wang, Jorge Lobo
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