Role Mining on Relational Data

Fine-grained access control for relational data defines user authorizations at the tuple level. Role Based Access Control (RBAC) has been proposed for relational data where roles are allowed access to tuples based on the authorized view defined by a selection predicate. During the last few years, extensive research has been conducted in the area of role en­ gineering. The existing approaches for role engineering are top-down (using domain experts), bottom-up (role-mining), or a hybrid of both. However, no research has been con­ ducted for role engineering in relational data. In this paper, we address this problem. The challenge is to extract an RBAC policy with authorized selection predicates for users given an existing tuple-level fine-grained access control pol­ icy. We formulate the problem for relational data, propose a role mining algorithm and conduct experimental evaluation. Experiments demonstrate that the proposed algorithm can achieve up to 400% improvement in performance for rela­ tional data as compared to existing role mining techniques.