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An Efficient Attribute-Based Access Control (ABAC) Policy Retrieval Method Based on Attribute and Value Levels in Multimedia Networks

机译:基于属性和值级别的多媒体网络中基于属性的有效访问控制(ABAC)策略检索方法

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摘要

Internet of Multimedia Things (IoMT) brings convenient and intelligent services while also bringing huge challenges to multimedia data security and privacy. Access control is used to protect the confidentiality and integrity of restricted resources. Attribute-Based Access Control (ABAC) implements fine-grained control of resources in an open heterogeneous IoMT environment. However, due to numerous users and policies in ABAC, access control policy evaluation is inefficient, which affects the quality of multimedia application services in the Internet of Things (IoT). This paper proposed an efficient policy retrieval method to improve the performance of access control policy evaluation in multimedia networks. First, retrieve policies that satisfy the request at the attribute level by computing based on the binary identifier. Then, at the attribute value level, the depth index was introduced to reconstruct the policy decision tree, thereby improving policy retrieval efficiency. This study carried out simulation experiments in terms of the different number of policies and different policy complexity situation. The results showed that the proposed method was three to five times more efficient in access control policy evaluation and had stronger scalability.
机译:多媒体物联网(IoMT)带来了便捷,智能的服务,同时也给多媒体数据的安全性和隐私性带来了巨大的挑战。访问控制用于保护受限资源的机密性和完整性。基于属性的访问控制(ABAC)在开放的异构IoMT环境中实现对资源的细粒度控制。但是,由于ABAC中有大量用户和策略,访问控制策略评估效率很低,这影响了物联网(IoT)中多媒体应用服务的质量。本文提出了一种有效的策略检索方法,以提高多媒体网络中访问控制策略评估的性能。首先,通过基于二进制标识符进行计算来检索在属性级别满足请求的策略。然后,在属性值级别上引入深度索引以重建策略决策树,从而提高策略检索效率。本研究针对不同数量的策略和不同的策略复杂性情况进行了模拟实验。结果表明,该方法在访问控制策略评估中的效率提高了三到五倍,并且具有更强的可扩展性。

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