Policy analysis for self-administrated role-based access control
Policy analysis for self-administrated role-based access control
Current techniques for security analysis of administrative role-based access control (ARBAC) policies restrict themselves to the separate administration assumption that essentially separates administrative roles from regular ones. The naive algorithm of tracking all users is all that is known for the security analysis of ARBAC policies without separate administration, and the state space explosion that this results in precludes building effective tools. In contrast, the separate administration assumption greatly simplifies the analysis since it makes it sufficient to track only one user at a time. However, separation limits the expressiveness of the models and restricts modeling distributed administrative control. In this paper, we undertake a fundamental study of analysis of ARBAC policies without the separate administration restriction, and show that analysis algorithms can be built that track only a bounded number of users, where the bound depends only on the number of administrative roles in the system. Using this fundamental insight paves the way for us to design an involved heuristic to further tame the state space explosion in practical systems. Our results are also very effective when applied on policies designed under the separate administration restriction. We implement our techniques and report on experiments conducted on several realistic case studies.
Ferrara, Anna Lisa
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Madhusudan, P.
8af89366-038f-4a30-9588-61d3f4477b49
Parlato, Gennaro
c28428a0-d3f3-4551-a4b5-b79e410f4923
16 March 2013
Ferrara, Anna Lisa
adbca2ba-5d6e-4888-975c-69b8f8fa461e
Madhusudan, P.
8af89366-038f-4a30-9588-61d3f4477b49
Parlato, Gennaro
c28428a0-d3f3-4551-a4b5-b79e410f4923
Ferrara, Anna Lisa, Madhusudan, P. and Parlato, Gennaro
(2013)
Policy analysis for self-administrated role-based access control.
TACAS 2013: 19th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS), Rome, Italy.
16 - 24 Mar 2013.
15 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Current techniques for security analysis of administrative role-based access control (ARBAC) policies restrict themselves to the separate administration assumption that essentially separates administrative roles from regular ones. The naive algorithm of tracking all users is all that is known for the security analysis of ARBAC policies without separate administration, and the state space explosion that this results in precludes building effective tools. In contrast, the separate administration assumption greatly simplifies the analysis since it makes it sufficient to track only one user at a time. However, separation limits the expressiveness of the models and restricts modeling distributed administrative control. In this paper, we undertake a fundamental study of analysis of ARBAC policies without the separate administration restriction, and show that analysis algorithms can be built that track only a bounded number of users, where the bound depends only on the number of administrative roles in the system. Using this fundamental insight paves the way for us to design an involved heuristic to further tame the state space explosion in practical systems. Our results are also very effective when applied on policies designed under the separate administration restriction. We implement our techniques and report on experiments conducted on several realistic case studies.
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ARBACpruning (2).pdf
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Published date: 16 March 2013
Venue - Dates:
TACAS 2013: 19th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS), Rome, Italy, 2013-03-16 - 2013-03-24
Organisations:
Electronic & Software Systems
Identifiers
Local EPrints ID: 344391
URI: http://eprints.soton.ac.uk/id/eprint/344391
PURE UUID: ad207299-9e6d-463c-ab2c-82c5cc86e843
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Date deposited: 21 Jan 2013 15:02
Last modified: 14 Mar 2024 12:12
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Contributors
Author:
Anna Lisa Ferrara
Author:
P. Madhusudan
Author:
Gennaro Parlato
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