A Survey of Role Mining

Mitra, Barsha; Sural, Shamik; Vaidya, Jaideep; Atluri, Vijayalakshmi

doi:10.1145/2871148

Cited by 84 publications

(70 citation statements)

References 96 publications

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“…[7,9,19,46,51,52,63]. In the literature the problem appears under different names like Discrete Basis Problem [51] or Minimal Noise Role Mining Problem [64,46,53]. P-Matrix Approximation.…”

Section: Low Boolean-rank Approximationmentioning

confidence: 99%

Parameterized low-rank binary matrix approximation

Fomin

Golovach

Panolan

2020

Data Min Knowl Disc

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We provide a number of algorithmic results for the following family of problems: For a given binary m × n matrix A and integer k, decide whether there is a "simple" binary matrix B which differs from A in at most k entries. For an integer r, the "simplicity" of B is characterized as follows.• Binary r-Means: Matrix B has at most r different columns. This problem is known to be NP-complete already for r = 2. We show that the problem is solvable in time 2 O(k log k) · (nm) O(1) and thus is fixed-parameter tractable parameterized by k. We prove that the problem admits a polynomial kernel when parameterized by r and k but it has no polynomial kernel when parameterized by k only unless NP ⊆ coNP /poly. We also complement these result by showing that when being parameterized by r and k, the problem admits an algorithm of running time 2 O(r· √ k log (k+r)) (nm) O(1) , which is subexponential in k for r ∈ O(k 1/2−ε ) for any ε > 0.• Low GF(2)-Rank Approximation: Matrix B is of GF(2)-rank at most r. This problem is known to be NP-complete already for r = 1. It also known to be W[1]-hard when parameterized by k. Interestingly, when parameterized by r and k, the problem is not only fixed-parameter tractable, but it is solvable in time 2 O(r 3/2 · √ k log k) (nm) O(1) , which is subexponential in k.• Low Boolean-Rank Approximation: Matrix B is of Boolean rank at most r. The problem is known to be NP-complete for k = 0 as well as for r = 1. We show that it is solvable in subexponential in k time 2 O(r2 r · √ k log k) (nm) O(1) .

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“…[7,9,19,46,51,52,63]. In the literature the problem appears under different names like Discrete Basis Problem [51] or Minimal Noise Role Mining Problem [64,46,53]. P-Matrix Approximation.…”

Section: Low Boolean-rank Approximationmentioning

confidence: 99%

Parameterized low-rank binary matrix approximation

Fomin

Golovach

Panolan

2020

Data Min Knowl Disc

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“…[7,8,12,27,29,30,38]. In the literature the problem appears under different names like Discrete Basis Problem [29] or Minimal Noise Role Mining Problem [27,31,39].…”

Section: Previous Workmentioning

confidence: 99%

“…Before proving the claim, let us show how it concludes the proof of the lemma. By (31) and Claim 5, we have that…”

mentioning

confidence: 93%

Approximation Schemes for Low-rank Binary Matrix Approximation Problems

Fomin

Golovach

Lokshtanov

et al. 2019

ACM Trans. Algorithms

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We provide a randomized linear time approximation scheme for a generic problem about clustering of binary vectors subject to additional constrains. The new constrained clustering problem encompasses a number of problems and by solving it, we obtain the first linear timeapproximation schemes for a number of well-studied fundamental problems concerning clustering of binary vectors and low-rank approximation of binary matrices. Among the problems solvable by our approach are Low GF(2)-Rank Approximation, Low Boolean-Rank Approximation, and various versions of Binary Clustering. For example, for Low GF(2)-Rank Approximation problem, where for an m × n binary matrix A and integer r > 0, we seek for a binary matrix B of GF(2) rank at most r such that 0 norm of matrix A − B is minimum, our algorithm, for any > 0 in time f (r, ) · n · m, where f is some computable function, outputs a (1+ )-approximate solution with probability at least (1− 1 e ). Our approximation algorithms substantially improve the running times and approximation factors of previous works. We also give (deterministic) PTASes for these problems running in time n f (r) 1 2 log 1 , where f is some function depending on the problem. Our algorithm for the constrained clustering problem is based on a novel sampling lemma, which is interesting in its own. * The research leading to these results have been supported by the Research Council of Norway via the projects "CLASSIS" and "MULTIVAL".

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“…The bottom-up approaches [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21] follow a similar principle of deriving the policy from the existing user-permission assignment. [22] provides a comprehensive literature survey on this topic. There has also been work on mining ABAC policies [4], [23], [24], [25].…”

Section: Related Workmentioning

confidence: 99%

Efficient Bottom-Up Mining of Attribute Based Access Control Policies

Talukdar

Batra

Vaidya

et al. 2017

2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)

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Attribute Based Access Control (ABAC) is fast replacing traditional access control models due to its dynamic nature, flexibility and scalability. ABAC is often used in collaborative environments. However, a major hurdle to deploying ABAC is to precisely configure the ABAC policy. In this paper, we present an ABAC mining approach that can automatically discover the appropriate ABAC policy rules. We first show that the ABAC mining problem is equivalent to identifying a set of functional dependencies in relational databases that cover all of the records in a table. We also propose a more efficient algorithm, called ABAC-SRM which discovers the most general policy rules from a set of candidate rules. We experimentally show that ABAC-SRM is accurate and significantly more efficient than the existing state of the art.

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A Survey of Role Mining

Cited by 84 publications

References 96 publications

Parameterized low-rank binary matrix approximation

Parameterized low-rank binary matrix approximation

Approximation Schemes for Low-rank Binary Matrix Approximation Problems

Efficient Bottom-Up Mining of Attribute Based Access Control Policies

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