Role Mining in the Presence of Noise
Role Mining in the Presence of Noise
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DOI：
10.1007/978-3-642-13739-6_7
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发表时间：
2010-06
期刊：
影响因子：
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通讯作者：
Jaideep Vaidya;V. Atluri;Qi Guo;Haibing Lu
中科院分区：
文献类型：
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作者：
Jaideep Vaidya;V. Atluri;Qi Guo;Haibing Lu
角色挖掘是从用户权限分配中自底向上发现角色的过程，近年来受到越来越多的关注。角色挖掘问题（RMP）和它的几个变种已经在文献中提出。虽然基本的RMP发现的角色完全代表UPA，不精确的变种，如δ-approxRMP和MinNoise-RMP，允许一些不精确的意义上说，发现的角色不一定要完全覆盖整个UPA。然而，由于真实的生活中的数据永远不会完全干净，角色挖掘过程只有在对噪声具有鲁棒性时才有效。本文件为解决这一问题迈出了第一步。我们在本文中的目标是检查是否可以改善UPA中的噪声的影响，由于在角色挖掘过程中的不精确性，从而对所发现的角色几乎没有负面影响。具体来说，我们定义了一个正式的噪声模型和实验评估以前提出的算法δ-近似RMP对噪声的鲁棒性。从本质上讲，这将允许人们在发现角色的同时提出策略，以最大限度地减少噪音的影响。我们在真实的数据上的实验表明，角色挖掘过程可以优先覆盖大量的真实的分配，并留下潜在的噪声分配进行进一步的检查。我们探讨了噪声数据的后果，并讨论了下一步如何提出更有效的算法来处理这些数据。
The problem of role mining, a bottom-up process of discovering roles from the user-permission assignments (UPA), has drawn increasing attention in recent years. The role mining problem (RMP) and several of its variants have been proposed in the literature. While the basic RMP discovers roles that exactly represent the UPA, theinexactvariants, such as theδ-approx RMP and MinNoise-RMP, allow for some inexactness in the sense that the discovered roles do not have to exactly cover the entire UPA. However, since data in real life is never completely clean, the role mining process is only effective if it is robust to noise. This paper takes the first step towards addressing this issue. Our goal in this paper is to examine if the effect of noise in the UPA could be ameliorated due to the inexactness in the role mining process, thus having little negative impact on the discovered roles. Specifically, we define a formal model of noise and experimentally evaluate the previously proposed algorithm forδ-approx RMP against its robustness to noise. Essentially, this would allow one to come up with strategies to minimize the effect of noise while discovering roles. Our experiments on real data indicate that the role mining process can preferentially cover a lot of the real assignments and leave potentially noisy assignments for further examination. We explore the ramifications of noisy data and discuss next steps towards coming up with more effective algorithms for handling such data.