Nonorthogonal decomposition of binary matrices for bounded-error data compression and analysis
ACM Transactions on Mathematical Software (TOMS)
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
Detecting and resolving policy misconfigurations in access-control systems
Proceedings of the 13th ACM symposium on Access control models and technologies
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A class of probabilistic models for role engineering
Proceedings of the 15th ACM conference on Computer and communications security
Automating role-based provisioning by learning from examples
Proceedings of the 14th ACM symposium on Access control models and technologies
Evaluating role mining algorithms
Proceedings of the 14th ACM symposium on Access control models and technologies
Optimal Boolean Matrix Decomposition: Application to Role Engineering
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Multi-assignment clustering for Boolean data
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A probabilistic approach to hybrid role mining
Proceedings of the 16th ACM conference on Computer and communications security
Fast algorithms for weighted bipartite matching
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
Towards an integrated approach to role engineering
Proceedings of the 3rd ACM workshop on Assurable and usable security configuration
Adversaries' Holy Grail: access control analytics
Proceedings of the First Workshop on Building Analysis Datasets and Gathering Experience Returns for Security
The minimum transfer cost principle for model-order selection
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Mining roles from web application usage patterns
TrustBus'11 Proceedings of the 8th international conference on Trust, privacy and security in digital business
Proceedings of the 17th ACM symposium on Access Control Models and Technologies
Role Mining with Probabilistic Models
ACM Transactions on Information and System Security (TISSEC)
Ensuring continuous compliance through reconciling policy with usage
Proceedings of the 18th ACM symposium on Access control models and technologies
Role mining algorithm evaluation and improvement in large volume android applications
Proceedings of the first international workshop on Security in embedded systems and smartphones
Toward mining of temporal roles
DBSec'13 Proceedings of the 27th international conference on Data and Applications Security and Privacy XXVII
Towards user-oriented RBAC model
DBSec'13 Proceedings of the 27th international conference on Data and Applications Security and Privacy XXVII
An optimization framework for role mining
Journal of Computer Security
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There has been increasing interest in automatic techniques for generating roles for role based access control, a process known as role mining. Most role mining approaches assume the input data is clean, and attempt to optimize the RBAC state. We examine role mining with noisy input data and suggest dividing the problem into two steps: noise removal and candidate role generation. We introduce an approach to use (non-binary) rank reduced matrix factorization to identify noise and experimentally show that it is effective at identifying noise in access control data. User- and permission-attributes can further be used to improve accuracy. Next, we show that our two-step approach is able to find candidate roles that are close to the roles mined from noise-less data. This method performs better than the approach of mining noisy data directly and offering the administrator increased control in the noise removal and candidate role generation phases. We note that our approach is applicable outside role engineering and may be used to identify errors or predict missing values in any access control matrix.