Minimizing the Number of Roles in Bottom-Up Role-Mining using Maximal Biclique Enumeration

TL;DR

This paper introduces a novel method for bottom-up role-mining by enumerating maximal bicliques, effectively handling practical inputs and providing exact solutions for many instances, with promising empirical results.

Contribution

The paper proposes a new biclique enumeration technique for role-mining that improves scalability and accuracy over previous methods, especially on challenging inputs.

Findings

Successfully solves over half of benchmark inputs exactly

Identifies large maximal bicliques as roles for hard instances

Empirical results show promising performance of the proposed methods

Abstract

Bottom-up role-mining is the determination of a set of roles given as input a set of users and the permissions those users possess. It is well-established in the research literature, and in practice, as an important problem in information security. A natural objective that has been explored in prior work is for the set of roles to be of minimum size. We address this problem for practical inputs while reconciling foundations, specifically, that the problem is \cnph. We first observe that an approach from prior work that exploits a sufficient condition for an efficient algorithm, while a useful first step, does not scale to more recently proposed benchmark inputs. We propose a new technique: the enumeration of maximal bicliques. We point out that the number of maximal bicliques provides a natural measure of the hardness of an input. We leverage the enumeration of maximal bicliques in two different ways. Our first approach addresses more than half the benchmark inputs to yield exact results. The other approach is needed for hard instances; in it, we identify and adopt as roles those that correspond to large maximal bicliques. We have implemented all our algorithms and carried out an extensive empirical assessment, which suggests that our approaches are promising. Our code is available publicly as open-source.