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基于层次属性的访问控制系统中的策略适配

Policy Adaptation in Hierarchical Attribute-Based Access Control Systems.

作者信息

DAS Saptarshi, Sural Shamik, Vaidya Jaideep, Atluri Vijayalakshmi

机构信息

Indian Institute of Technology Kharagpur, India.

Rutgers Business School, USA.

出版信息

ACM Trans Internet Technol. 2019 Nov;19(3). doi: 10.1145/3323233.

Abstract

In Attribute-Based Access Control (ABAC), access to resources is given based on the attributes of subjects, objects, and environment. There is an imminent need for the development of efficient algorithms that enable migration to ABAC. However, existing policy mining approaches do not consider possible adaptation to the policy of a similar organization. In this article, we address the problem of automatically determining an optimal assignment of attribute values to subjects for enabling the desired accesses to be granted while minimizing the number of ABAC rules used by each subject or other appropriate metrics. We show the problem to be NP-Complete and propose a heuristic solution.

摘要

在基于属性的访问控制(ABAC)中,对资源的访问是基于主体、客体和环境的属性来授予的。迫切需要开发能够实现向ABAC迁移的高效算法。然而,现有的策略挖掘方法没有考虑到对类似组织策略的可能适应性。在本文中,我们解决了自动确定主体属性值的最优分配问题,以便在最小化每个主体使用的ABAC规则数量或其他适当指标的同时,授予所需的访问权限。我们证明该问题是NP完全问题,并提出了一种启发式解决方案。

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引用本文的文献

1
PAMMELA: Policy Administration Methodology using Machine Learning.
SECRYPT. 2022 Jul;2022:147-157. doi: 10.5220/0011272400003283.

本文引用的文献

1
Efficient bottom-up Mining of Attribute Based Access Control Policies.
IEEE Conf Collab Internet Comput. 2017 Oct;2017:339-348. doi: 10.1109/CIC.2017.00051. Epub 2017 Dec 14.

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