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An improved minimal noise role mining algorithm based on role interpretability
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AI
DOI：10.1016/j.cose.2023.103100.png)
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Abstract
En 中文
Interpretable role mining has achieved notable improvements in usability and effectiveness of roles in RBAC deployments, owing to its virtue in mining meaningful roles. However, current research ignores the interference caused by data noise, thus limiting broader application and deployment in real scenar-ios. In this paper, the interpretable role mining problem is extended considering data noise, referencing the minimal noise role mining problem. Accordingly, an improved minimal noise role mining algorithm is proposed to optimize the reconstruction error and role interpretability. The experimental results on real data demonstrate that the proposed algorithm has better efficiency, lower reconstruction error while ensuring the interpretability regardless of the data scale.(c) 2023 Elsevier Ltd. All rights reserved.
Keywords:
Role mining
Role -based access control
Role interpretability
Minimal noise algorithm
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