Mining Hierarchical Temporal Roles with Multiple Metrics

Scott D. Stoller; Thang Bui

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Mining Hierarchical Temporal Roles with Multiple Metrics

Scott D. Stoller, Thang Bui

Cryptography and Security · Submitted 8 Mar 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In experiments with datasets based on real-world ACL policies, our algorithm is more effective than previous algorithms at optimizing policy quality.

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What the abstract itself states, as probabilities: claims first 0.96 · states limitation 0.10 · human participants 0.03 · controlled comparison 0.57 · randomized assignment 0.01 · states quantitative result 0.07 · compares with prior results 0.95 · mentions code or data availability 0.03.

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arXiv listing abstract v7. Results and the main claim are underlined; show every role.

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Temporal role-based access control (TRBAC) extends role-based access control to limit the times at which roles are enabled.	background 1.00	0.03	0.02	asserted 1.00
s1	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.	contribution 1.00	0.90	0.13	asserted 1.00
s2	Such algorithms have potential to significantly reduce the cost of migration from timed ACLs to TRBAC.	interpretation 0.70	0.08	0.08	hedged possibility 0.98
s3	The algorithm is parameterized by the policy quality metric.	method 0.97	0.64	0.03	asserted 1.00
s4	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.	method 0.66	0.74	0.05	asserted 0.96
s5	Ours is the first TRBAC policy mining algorithm that produces hierarchical policies, and the first that optimizes weighted structural complexity or interpretability.	contribution 0.97	0.90	0.30	asserted 1.00
s6	In experiments with datasets based on real-world ACL policies, our algorithm is more effective than previous algorithms at optimizing policy quality.	result 1.00	0.96	0.71	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.92.

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arXiv listing abstract v7

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

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.

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