Constraint-Aware Role Mining via Extended Boolean Matrix Decomposition


The role mining problem has received considerable attention recently. Among the various solutions proposed, the Boolean matrix decomposition (BMD) formulation has stood out, that primarily discovers roles by decomposing the binary matrix representing user-to-permission assignment (UPA) into 2 matrices—user-to-role assignment (UA) and permission-to-role assignment (PA). But, supporting certain embedded constraints, like separation of duty (SoD) and exceptions, is important to the role mining method. Otherwise, the mined roles may not capture the inherent constraints of the access control policies of the organization. Not one of the previously proposed role mining solutions, together with BMD, take into consideration these underlying constraints while mining. During this paper, we have a tendency to extend the BMD therefore that it reflects such embedded constraints by proposing to permit negative permissions in roles or negative role assignments for users. Specifically, by allowing negative permissions in roles, we tend to are typically in a position to use less roles to reconstruct the identical given user-permission assignments. Moreover, from the resultant roles we tend to can discover underlying constraints such as separation of duty constraints. This feature is not supported by any existing role mining approaches. Hence, we tend to decision the role mining drawback with negative authorizations the constraint-aware role mining drawback (CRM). We have a tendency to additionally explore other attention-grabbing variants of the CRM, which might occur in real situations. To enable CRM and its variants, we tend to propose a unique approach, extended Boolean matrix decomposition (EBMD), that addresses the ineffectiveness of BMD in its ability of capturing underlying constraints. We tend to analyze the computational complexity for every of CRM variants and gift heuristics for issues that are proven to be NP-laborious.

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