TY - GEN
T1 - Mining hierarchical temporal roles with multiple metrics
AU - Stoller, Scott D.
AU - Bui, Thang
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2016.
PY - 2016
Y1 - 2016
N2 - Temporal role-based access control (TRBAC) extends rolebased 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 their goal of minimizing the number of roles.
AB - Temporal role-based access control (TRBAC) extends rolebased 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 their goal of minimizing the number of roles.
UR - https://www.scopus.com/pages/publications/84979498046
U2 - 10.1007/978-3-319-41483-6_6
DO - 10.1007/978-3-319-41483-6_6
M3 - Conference contribution
AN - SCOPUS:84979498046
SN - 9783319414829
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 79
EP - 95
BT - Data and Applications Security and Privacy - 30th Annual IFIP WG 11.3 Conference, DBSec 2016, Proceedings
A2 - Ranise, Silvio
A2 - Swarup, Vipin
PB - Springer Verlag
T2 - 30th IFIP WG 11.3 Conference on Data and Applications Security, DBSec 2016
Y2 - 18 July 2016 through 20 July 2016
ER -