Constrained role mining aims to define a valid set of roles efficiently representing the organization of a company, easing the management of the security policies. Since the associated problems are NP hard, usually some heuristics are defined to find some sub-optimal solutions. In this paper we define two heuristics for the Permission Distribution and Role Usage Cardinality Constraints in the post processing framework, i.e. refining the roles produced by some other algorithm. We discuss the performance of the proposed heuristics applying them to some standard datasets showing the improvements w.r.t. previously available solutions.

PostProcessing in Constrained Role Mining / C. Blundo, S. Cimato, L. Siniscalchi (LECTURE NOTES IN COMPUTER SCIENCE). - In: Intelligent Data Engineering and Automated Learning – IDEAL 2018 / [a cura di] H. Yin, D. Camacho, P. Novais, A.J. Tallón-Ballesteros. - [s.l] : Springer, 2018. - ISBN 9783030034924. - pp. 204-214 (( Intervento presentato al 19. convegno International Conference on Intelligent Data Engineering and Automated Learning – IDEAL 2018 tenutosi a Madrid nel 2018.

PostProcessing in Constrained Role Mining

S. Cimato;
2018

Abstract

Constrained role mining aims to define a valid set of roles efficiently representing the organization of a company, easing the management of the security policies. Since the associated problems are NP hard, usually some heuristics are defined to find some sub-optimal solutions. In this paper we define two heuristics for the Permission Distribution and Role Usage Cardinality Constraints in the post processing framework, i.e. refining the roles produced by some other algorithm. We discuss the performance of the proposed heuristics applying them to some standard datasets showing the improvements w.r.t. previously available solutions.
Settore INF/01 - Informatica
2018
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/602221
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