Role-Based Access Control Models
Computer
RBAC '95 Proceedings of the first ACM Workshop on Role-based access control
RBAC '00 Proceedings of the fifth ACM workshop on Role-based access control
Proposed NIST standard for role-based access control
ACM Transactions on Information and System Security (TISSEC)
A scenario-driven role engineering process for functional RBAC roles
SACMAT '02 Proceedings of the seventh ACM symposium on Access control models and technologies
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Proceedings of the tenth ACM symposium on Access control models and technologies
RoleMiner: mining roles using subset enumeration
Proceedings of the 13th ACM conference on Computer and communications security
Role engineering using graph optimisation
Proceedings of the 12th ACM symposium on Access control models and technologies
The role mining problem: finding a minimal descriptive set of roles
Proceedings of the 12th ACM symposium on Access control models and technologies
Fast exact and heuristic methods for role minimization problems
Proceedings of the 13th ACM symposium on Access control models and technologies
Migrating to optimal RBAC with minimal perturbation
Proceedings of the 13th ACM symposium on Access control models and technologies
Mining roles with semantic meanings
Proceedings of the 13th ACM symposium on Access control models and technologies
The Role Hierarchy Mining Problem: Discovery of Optimal Role Hierarchies
ACSAC '08 Proceedings of the 2008 Annual Computer Security Applications Conference
HyDRo --- Hybrid Development of Roles
ICISS '08 Proceedings of the 4th International Conference on Information Systems Security
Edge-RMP: Minimizing administrative assignments for role-based access control
Journal of Computer Security
Optimal Boolean Matrix Decomposition: Application to Role Engineering
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A probabilistic approach to hybrid role mining
Proceedings of the 16th ACM conference on Computer and communications security
Role Engineering via Prioritized Subset Enumeration
IEEE Transactions on Dependable and Secure Computing
A serially addressable, flexible current monitor for test fixture based IDDQ/ISSQtesting
ITC'94 Proceedings of the 1994 international conference on Test
Mining roles from web application usage patterns
TrustBus'11 Proceedings of the 8th international conference on Trust, privacy and security in digital business
Towards user-oriented RBAC model
DBSec'13 Proceedings of the 27th international conference on Data and Applications Security and Privacy XXVII
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The problem of role mining, a bottom-up process of discovering roles from the user-permission assignments (UPA), has drawn increasing attention in recent years. The role mining problem (RMP) and several of its variants have been proposed in the literature. While the basic RMP discovers roles that exactly represent the UPA, the inexact variants, such as the δ-approx RMP and MinNoise-RMP, allow for some inexactness in the sense that the discovered roles do not have to exactly cover the entire UPA. However, since data in real life is never completely clean, the role mining process is only effective if it is robust to noise. This paper takes the first step towards addressing this issue. Our goal in this paper is to examine if the effect of noise in the UPA could be ameliorated due to the inexactness in the role mining process, thus having little negative impact on the discovered roles. Specifically, we define a formal model of noise and experimentally evaluate the previously proposed algorithm for δ-approx RMP against its robustness to noise. Essentially, this would allow one to come up with strategies to minimize the effect of noise while discovering roles. Our experiments on real data indicate that the role mining process can preferentially cover a lot of the real assignments and leave potentially noisy assignments for further examination. We explore the ramifications of noisy data and discuss next steps towards coming up with more effective algorithms for handling such data.