Taming role mining complexity in RBAC

In this paper we address the problem of reducing the role mining complexity in RBAC systems. To this aim, we propose a three steps methodology: first, we associate a weight to roles; second, we identify user-permission assignments that cannot belong to roles with a weight exceeding a given threshold; and third, we restrict the role-finding problem to user-permission assignments identified in the second step. We formally show—the proofs of our results are rooted in graph theory—that this methodology allows role engineers for the elicitation of stable candidate roles, by contextually simplifying the role selection task. Efficient algorithms to implement our strategy are also described. Further, we discuss practical applications of our approach. Finally, we tested our methodology on real dataset. Results achieved confirm both the viability of our proposal and the analytical findings.