Towards a General Framework for Optimal Role Mining

Abstract

Role Based Access Control (RBAC) is the most widely used advanced access control model deployed in a variety of organizations. To deploy an RBAC system, one needs to first identify a complete set of roles, including permission role assignments and role user assignments. This process, known as role engineering, has been identified as one of the costliest tasks in migrating to RBAC. Since many organizations already have some form of user permission assignments defined, it makes sense to identify roles from this existing information. This process, known as role mining, has gained significant interest in recent years and numerous role mining techniques have been developed that take into account the characteristics of the core RBAC model, as well as its various extended features and each is based on a specific optimization metric. In this paper, we propose a generic approach which transforms the role mining problem into a constraint satisfaction problem. The transformation allows us to discover the optimal RBAC state based on customized optimization metrics. We also extend the RBAC model to include more context-aware and application specific constraints. These extensions broaden the applicability of the model beyond the classic role mining to include features such as permission usage, hierarchical role mining, hybrid role engineering approaches, and temporal RBAC models. We also perform experiments to show applicability and effectiveness of the proposed approach.