.png){kind=icon}
返回
Mining temporal roles using many-valued concepts
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cose.2016.04.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many of today's access control policies are associated with temporal restrictions. Under temporal role-based access control (TRBAC), roles have an associated temporal component, which allows them to better encapsulate such temporal access control policies. However, given their complexity, TRBAC systems can only be well managed if the set of roles is correctly defined. The process of deriving an optimal set of such roles is known as temporal role mining. In this paper, we formally define the temporal role mining problem (TRMP) in the form of a matrix decomposition problem, by introducing a new operator that multiplies a set with a Boolean value and redefining existing matrix multiplication operations in terms of it. We also define a new metric for temporal role mining, called cumulative overhead of temporal roles and permissions (CO-TRAP), which takes into consideration the administrative effort required for managing the resulting TRBAC system. Since TRMP as well as minimization of CO-TRAP are NP-complete problems, we propose two greedy algorithms based on many valued concepts. Experimental evaluation on a number of real-world datasets shows that the proposed approach is both efficient and effective. (C) 2016 Elsevier Ltd. All rights reserved.
Keyword:
TRBAC
Temporal role mining
Many-valued concept
Matrix decomposition
Access control
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
5.4
论文数:
4.6K
被引数:
1.4W
机构
引用论文
.png){kind=icon}
Genetic characterization of flesh browning trait in apple using the non-browning cultivar ‘Aori 27’
PREAI
分享收藏
查看更多内容