Mining Hierarchical Temporal Roles with Multiple Metrics
This work addresses the cost of migrating from timed ACLs to TRBAC for access control systems, offering a novel algorithm with incremental improvements in policy quality optimization.
The paper tackles the problem of mining high-quality temporal role-based access control (TRBAC) policies from timed ACLs, introducing an algorithm that produces hierarchical policies and optimizes multiple quality metrics, showing improved effectiveness over previous methods in experiments with real-world datasets.
Temporal role-based access control (TRBAC) extends role-based access control to limit the times at which roles are enabled. This paper presents a new algorithm for mining high-quality TRBAC policies from timed ACLs (i.e., ACLs with time limits in the entries) and optionally user attribute information. Such algorithms have potential to significantly reduce the cost of migration from timed ACLs to TRBAC. The algorithm is parameterized by the policy quality metric. We consider multiple quality metrics, including number of roles, weighted structural complexity (a generalization of policy size), and (when user attribute information is available) interpretability, i.e., how well role membership can be characterized in terms of user attributes. Ours is the first TRBAC policy mining algorithm that produces hierarchical policies, and the first that optimizes weighted structural complexity or interpretability. In experiments with datasets based on real-world ACL policies, our algorithm is more effective than previous algorithms at optimizing policy quality.