Optimal Boolean Matrix Decomposition: Application to Role Engineering

Lu, Haibing; Vaidya, Jaideep; Atluri, Vijayalakshmi

doi:10.1109/icde.2008.4497438

Cited by 133 publications

(158 citation statements)

References 12 publications

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“…The problem of mining large tiles has already been tackled in [10,16,13]. The authors of the two first papers show that the problem of finding roles is equivalent to variants of the tiling database problem.…”

Section: Related Workmentioning

confidence: 99%

“…It has been shown that in some applications such as in role mining [10,16] where the idea is, given a set of users and a set of permissions, to find a minimum set of roles such that all users will be assigned a role for which some permissions will be granted, mining roles is equivalent to a variant of mining itemsets with large area in a database.…”

Section: Introductionmentioning

confidence: 99%

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Mining Top-K Largest Tiles in a Data Stream

Lam

Pei

Prado

et al. 2014

Machine Learning and Knowledge Discovery in Databases

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Abstract. Large tiles in a database are itemsets with the largest area which is defined as the itemset frequency in the database multiplied by its size. Mining these large tiles is an important pattern mining problem since tiles with a large area describe a large part of the database. In this paper, we introduce the problem of mining top-k largest tiles in a data stream under the sliding window model. We propose a candidatebased approach which summarizes the data stream and produces the top-k largest tiles efficiently for moderate window size. We also propose an approximation algorithm with theoretical bounds on the error rate to cope with large size windows. In the experiments with two real-life datasets, the approximation algorithm is up to hundred times faster than the candidate-based solution and the baseline algorithms based on the state-of-the-art solutions. We also investigate an application of large tile mining in computer vision and in emerging search topics monitoring.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining Top-K Largest Tiles in a Data Stream

Lam

Pei

Prado

et al. 2014

Machine Learning and Knowledge Discovery in Databases

View full text Add to dashboard Cite

show abstract

“…Another approach to the role mining is explained by Vaidya et al [17], that is based on the analysis of all possible intersections among permissions possessed by the users. Only recently researchers have started to formalize the role-set optimality concept, defining "interestingness" metric for roles [18,11,12]. Indeed, the importance of role completeness and role management efficiency resulting from the role engineering process has been evident from the earliest papers on the subject.…”

Section: Related Workmentioning

confidence: 99%

“…They also demonstrated that basicRMP is NP-complete. As shown in [6], not only is the basicRMP problem equivalent to the minimum clique covering, but it can be reduced to many other NP-complete problems, like binary matrices factorization [14,11] and tiling database [7] to cite a few.…”

Section: Related Workmentioning

confidence: 99%

Mining Stable Roles in RBAC

Colantonio

Pietro

Ocello³

et al. 2009

Emerging Challenges for Security, Privacy and Trust

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Abstract. In this paper we address the problem of generating a candidate role-set for an RBAC configuration that enjoys the following two key features: it minimizes the administration cost; and, it is a stable candidate role-set. To achieve these goals, we implement a three steps methodology: first, we associate a weight to roles; second, we identify and remove the user-permission assignments that cannot belong to a role that have a weight exceeding a given threshold; third, we restrict the problem of finding a candidate role-set for the given system configuration using only the user-permission assignments that have not been removed in the second stepthat is, user-permission assignments that belong to roles with a weight exceeding the given threshold. We formally show-proof of our results are rooted in graph theory-that this methodology achieves the intended goals. Finally, we discuss practical applications of our approach to the role mining problem.

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“…The authors present two algorithms: CompleteMiner and FastMiner to enumerate the set of candidate roles. Lu et al [9] model the problem of Optimal Boolean Matrix Decomposition (OBMD) using binary integer programming. This enables them to directly apply a wide range of heuristics for binary integer programming to solve the OBMD problem.…”

Section: Related Workmentioning

confidence: 99%

Toward Mining of Temporal Roles

Mitra

Sural

Atluri

et al. 2013

Lecture Notes in Computer Science

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Abstract. In Role-Based Access Control (RBAC), users acquire permissions through their assigned roles. Role mining, the process of finding a set of roles from direct user-permission assignments, is essential for successful implementation of RBAC. In many organizations it is often required that users are given permissions that can vary with time. To handle such requirements, temporal extensions of RBAC like Temporal-RBAC (TRBAC) and Generalized Temporal Role-Based Access Control (GTRBAC) have been proposed. Existing role mining techniques, however, cannot be used to process the temporal element associated with roles in these models. In this paper, we propose a method for mining roles in the context of TRBAC. First we formally define the Temporal Role Mining Problem (TRMP), and then show that the TRMP problem is NP-complete and present a heuristic approach for solving it.

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Optimal Boolean Matrix Decomposition: Application to Role Engineering

Cited by 133 publications

References 12 publications

Mining Top-K Largest Tiles in a Data Stream

Mining Top-K Largest Tiles in a Data Stream

Mining Stable Roles in RBAC

Toward Mining of Temporal Roles

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