While many role mining algorithms have been proposed in recent years, there lacks a comprehensive study to compare these algorithms. These role mining algorithms have been evaluated when they were proposed, but the evaluations were using different datasets and evaluation criteria. In this paper, we introduce a comprehensive framework for evaluating role mining algorithms. We categorize role mining algorithms into two classes based on their outputs; Class 1 algorithms output a sequence of prioritized roles while Class 2 algorithms output complete RBAC states. We then develop techniques that enable us to compare these algorithms directly. We also introduce a new role mining algorithm and two new ways for algorithmically generating datasets for evaluation. Using synthetic as well as real datasets, we compared nine role mining algorithms. Our results illustrate the strengths and weaknesses of these algorithms.
%0 Conference Paper
%1 1542224
%A Molloy, Ian
%A Li, Ninghui
%A Li, Tiancheng
%A Mao, Ziqing
%A Wang, Qihua
%A Lobo, Jorge
%B SACMAT '09: Proceedings of the 14th ACM symposium on Access control models and technologies
%C New York, NY, USA
%D 2009
%I ACM
%K imported
%P 95--104
%R http://doi.acm.org/10.1145/1542207.1542224
%T Evaluating role mining algorithms
%U http://portal.acm.org/citation.cfm?id=1542224&coll=GUIDE&dl=GUIDE&CFID=50752827&CFTOKEN=30184487&ret=1#Fulltext
%X While many role mining algorithms have been proposed in recent years, there lacks a comprehensive study to compare these algorithms. These role mining algorithms have been evaluated when they were proposed, but the evaluations were using different datasets and evaluation criteria. In this paper, we introduce a comprehensive framework for evaluating role mining algorithms. We categorize role mining algorithms into two classes based on their outputs; Class 1 algorithms output a sequence of prioritized roles while Class 2 algorithms output complete RBAC states. We then develop techniques that enable us to compare these algorithms directly. We also introduce a new role mining algorithm and two new ways for algorithmically generating datasets for evaluation. Using synthetic as well as real datasets, we compared nine role mining algorithms. Our results illustrate the strengths and weaknesses of these algorithms.
%@ 978-1-60558-537-6
@inproceedings{1542224,
abstract = {While many role mining algorithms have been proposed in recent years, there lacks a comprehensive study to compare these algorithms. These role mining algorithms have been evaluated when they were proposed, but the evaluations were using different datasets and evaluation criteria. In this paper, we introduce a comprehensive framework for evaluating role mining algorithms. We categorize role mining algorithms into two classes based on their outputs; Class 1 algorithms output a sequence of prioritized roles while Class 2 algorithms output complete RBAC states. We then develop techniques that enable us to compare these algorithms directly. We also introduce a new role mining algorithm and two new ways for algorithmically generating datasets for evaluation. Using synthetic as well as real datasets, we compared nine role mining algorithms. Our results illustrate the strengths and weaknesses of these algorithms.},
added-at = {2009-11-24T14:19:12.000+0100},
address = {New York, NY, USA},
author = {Molloy, Ian and Li, Ninghui and Li, Tiancheng and Mao, Ziqing and Wang, Qihua and Lobo, Jorge},
biburl = {https://www.bibsonomy.org/bibtex/22893e55b60c99ec12116ee934c0e1532/anneba},
booktitle = {SACMAT '09: Proceedings of the 14th ACM symposium on Access control models and technologies},
description = {Evaluating role mining algorithms},
doi = {http://doi.acm.org/10.1145/1542207.1542224},
interhash = {1c388dddf678c38645a4289443f13f8c},
intrahash = {2893e55b60c99ec12116ee934c0e1532},
isbn = {978-1-60558-537-6},
keywords = {imported},
location = {Stresa, Italy},
pages = {95--104},
publisher = {ACM},
timestamp = {2009-11-24T14:19:13.000+0100},
title = {Evaluating role mining algorithms},
url = {http://portal.acm.org/citation.cfm?id=1542224&coll=GUIDE&dl=GUIDE&CFID=50752827&CFTOKEN=30184487&ret=1#Fulltext},
year = 2009
}