A Scalable Role Mining Approach for Large Organizations

Role-based access control (RBAC) model has gained significant attention in cybersecurity in recent years. RBAC restricts system access only to authorized users based on the roles and regulations within an organization. The flexibility and usability of this model have encouraged organizations to migrate from traditional discretionary access control (DAC) models to RBAC. However, this transition requires accomplishing a very challenging task called role mining in which users' roles are generated from the existing access control lists. Although various approaches have been proposed to address this NP-complete problem in the literature, they suffer either from low scalability such that their execution time increases exponentially with the input size, or they rely on fast heuristics with low optimality that generate too many roles. In this paper, we introduce a highly scalable yet optimal approach to tackle the role mining problem. To this end, we utilize a non-negative rank reduced matrix decomposition method to decompose a large-scale user-permission assignment into two constitutive components, i.e. the user-role and role-permission assignments. Then, we apply a thresholding technique to convert real-valued components into binary-valued factors. We employ various access control configurations and demonstrate that our proposed model is able to effectively discover the latent relationship behind the user-permission data even with large datasets.