一种增强高协同访问控制系统的角色映射算法

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Distributed and Parallel Databases Pub Date : 2022-03-31 DOI:10.1007/s10619-022-07407-9
Doaa Abdelfattah, Hesham A. Hassan, Fatma A. Omara
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引用次数: 0

摘要

不同组织之间的协作被认为是将应用程序和服务迁移到云计算环境的主要好处之一。不幸的是,这种合作带来了许多挑战,例如未经授权的人访问敏感资源。基于角色的访问控制(RBAC)模型通常部署在大型组织中。本文研究了在线存储规则的可扩展性问题。由于同一云环境中共享资源和/或协作组织数量的增加,该问题会影响访问控制系统的性能。因此,本文建议用RTR (Role-To-Role)映射规则取代跨域RBAC规则。RTR映射规则使用新提出的角色映射算法生成。对比研究了该算法在规则存储大小和授权响应时间方面的性能。结果表明,该算法减少了存储规则的数量,使Rule-Store大小最小化,缩短了授权响应时间。此外,本文还提出了在RTR映射模型上应用并发方法,使用所提出的Role-Mapping算法来节省更多的授权响应时间。因此,它将适用于高度协作的云环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A novel role-mapping algorithm for enhancing highly collaborative access control system

The collaboration among different organizations is considered one of the main benefits of moving applications and services to a cloud computing environment. Unfortunately, this collaboration raises many challenges such as the access of sensitive resources by unauthorized people. Usually, Role-Based Access-Control (RBAC) model is deployed in large organizations. This paper addresses the scalability problem of the online stored rules. This problem affects the performance of the access control system due to increasing number of shared resources and/or number of collaborating organizations in the same cloud environment. Therefore, this paper proposes replacing the cross-domain RBAC rules with Role-To-Role (RTR) mapping rules among all organizations. The RTR mapping rules are generated using a newly proposed Role-Mapping algorithm. A comparative study is performed to evaluate the proposed algorithm’s performance with concerning the Rule-Store size and the authorization response time. According to the results, it is found that the proposed algorithm reduces the number of stored rules which minimizes the Rule-Store size and reduces the authorization response time. Additionally, this paper proposes applying a concurrent approach on the RTR mapping model using the proposed Role-Mapping algorithm to achieve more savings in the authorization response time. Therefore, it will be suitable in highly-collaborative cloud environments.

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来源期刊
Distributed and Parallel Databases
Distributed and Parallel Databases 工程技术-计算机:理论方法
CiteScore
3.50
自引率
0.00%
发文量
17
审稿时长
>12 weeks
期刊介绍: Distributed and Parallel Databases publishes papers in all the traditional as well as most emerging areas of database research, including: Availability and reliability; Benchmarking and performance evaluation, and tuning; Big Data Storage and Processing; Cloud Computing and Database-as-a-Service; Crowdsourcing; Data curation, annotation and provenance; Data integration, metadata Management, and interoperability; Data models, semantics, query languages; Data mining and knowledge discovery; Data privacy, security, trust; Data provenance, workflows, Scientific Data Management; Data visualization and interactive data exploration; Data warehousing, OLAP, Analytics; Graph data management, RDF, social networks; Information Extraction and Data Cleaning; Middleware and Workflow Management; Modern Hardware and In-Memory Database Systems; Query Processing and Optimization; Semantic Web and open data; Social Networks; Storage, indexing, and physical database design; Streams, sensor networks, and complex event processing; Strings, Texts, and Keyword Search; Spatial, temporal, and spatio-temporal databases; Transaction processing; Uncertain, probabilistic, and approximate databases.
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