Role Mining Heuristics for Permission-Role-Usage Cardinality Constraints

Autor: Stelvio Cimato, Luisa Siniscalchi, Carlo Blundo
Rok vydání: 2021
Předmět:
Zdroj: Blundo, C, Cimato, S & Siniscalchi, L 2022, ' Role Mining Heuristics for Permission-Role-Usage Cardinality Constraints ', Computer Journal, vol. 65, no. 6, pp. 1386-1411 . https://doi.org/10.1093/comjnl/bxaa186
ISSN: 1460-2067
0010-4620
DOI: 10.1093/comjnl/bxaa186
Popis: Role-based access control (RBAC) has become a de facto standard to control access to restricted resources in complex systems and is widely deployed in many commercially available applications, including operating systems, databases and other softwares. The migration process towards RBAC, starting from the current access configuration, relies on the design of role mining techniques, whose aim is to define suitable roles that implement the given access policies. Some constraints can be used to transform the roles automatically output by the mining procedures and effectively capture the organization’s status under analysis. Such constraints can limit the final configuration characteristics, such as the number of roles assigned to a user, or the number of permissions included in a role, and produce a resulting role set that is effectively usable in real-world situations. In this paper, we consider two constraints: the number of permissions a role can include and the number of roles assigned to any user. In particular, we present two heuristics that produce roles compliant with both constraints and evaluate their performances using both real-world and synthetic datasets.
Databáze: OpenAIRE
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