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Towards User-Oriented RBAC Model

机译:面向用户的RBAC模型

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Role mining recently has attracted much attention from the role-based access control (RBAC) research community as it provides a machine-operated means of discovering roles from existing permission assignments. While there is a rich body of literature on role mining, we find that user experience/perception - one ultimate goal for any information system - is surprisingly ignored by the existing works. This work is the first to study role mining from the end-user perspective. Specifically, based on the observation that end-users prefer simple role assignments, we propose to incorporate to the role mining process a user-role assignment sparseness constraint that mandates the maximum number of roles each user can have. Under this rationale, we formulate user-oriented role mining as two specific problems: one is user-oriented exact role mining problem (RMP), which is obliged to completely reconstruct the given permission assignments, and the other is user-oriented approximate RMP, which tolerates a certain amount of deviation from the complete reconstruction. The extra sparseness constraint poses a great challenge to role mining, which in general is already a hard problem. We examine some typical existing role mining methods to see their applicability to our problems. In light of their insufficiency, we present a new algorithm, which is based on a novel dynamic candidate role generation strategy, tailored to our problems. Experiments on benchmark datasets demonstrate the effectiveness of our proposed algorithm.
机译:角色挖掘最近吸引了基于角色的访问控制(RBAC)研究社区的广泛关注,因为它提供了一种机器操作的方法来从现有权限分配中发现角色。尽管关于角色挖掘的文献很多,但我们发现用户体验/感知(任何信息系统的终极目标)都被现有作品意外地忽略了。这项工作是从最终用户角度研究角色挖掘的第一项工作。具体而言,基于观察到最终用户更喜欢简单的角色分配,我们建议将角色角色稀疏性约束纳入角色挖掘过程,以约束每个用户可以拥有的最大角色数。在此基础上,我们将面向用户的角色挖掘公式化为两个具体问题:一个是面向用户的精确角色挖掘问题(RMP),它必须完全重构给定的权限分配,另一个是面向用户的近似RMP,可以容忍与完整重建之间的一定程度的偏差。额外的稀疏约束对角色挖掘提出了巨大挑战,而角色挖掘通常已经是一个难题。我们研究了一些典型的现有角色挖掘方法,以了解它们对我们的问题的适用性。鉴于他们的不足,我们提出了一种新算法,该算法基于针对我们问题的新颖动态候选角色生成策略。在基准数据集上进行的实验证明了我们提出的算法的有效性。

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