基于合规性和反馈的模型来衡量托管数字双胞胎的云可信度

Syed Imran Akhtar, Abdul Rauf, Haider Abbas, Muhammad Faisal Amjad, Ifra Batool
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引用次数: 0

摘要

基于云计算的数字孪生利用来自各种数据源的实时数据来模拟其物理对应物的行为和性能,从而实现监控和分析。然而,将云计算用于数字孪生的一个限制因素是用户对其数据安全的担忧。这些数据可能位于云中的任何地方,用户对其安全性的控制非常有限。基于云计算的数字孪生为研究人员提供了合作机会,但此类数字孪生的安全性需要采取云计算特有的措施。为了克服这一缺陷,我们需要设计一种机制,它不仅能确保基本的安全保障,还能计算出云服务提供商(CSP)的可信度值。这将增强云用户的信心,使他们能够选择合适的 CSP 进行数据相关交互。本研究提出了一种解决方案,即根据数据安全控制合规性、用户反馈和审计员评级来计算 CSP 的可信度。此外,还加入了两个附加因素,即合规性测量的准确性和控制重要性因素,以满足其他非标准条件的要求。我们实施了数据安全合规性监控和数据信任即服务,并使用了三种 CSP(每种 CSP 有十种不同的设置),通过所设计的公式支持了我们的主张。实验结果表明,随着合规水平、用户反馈和审计员评级的变化,可信度值也会发生变化。合规性更好的 CSP 具有更好的可信度值。但是,如果合规性测量准确度和控制重要性系数较低,可信度也会相应降低。这为我们的计算提供了平衡和现实性。这个模型很独特,有助于建立用户对基于云的数字孪生的信任。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Compliance and feedback based model to measure cloud trustworthiness for hosting digital twins
Cloud-based digital twins use real-time data from various data sources to simulate the behavior and performance of their physical counterparts, enabling monitoring and analysis. However, one restraining factor in the use of cloud computing for digital twins is its users’ concerns about the security of their data. This data may be located anywhere in the cloud, with very limited control of the user to ensure its security. Cloud-based digital twins provide opportunities for researchers to collaborate yet security of such digital twins requires measures specific to cloud computing. To overcome this shortcoming, we need to devise a mechanism that not only ensures essential security safeguards but also computes a Trustworthiness value for Cloud Service Providers (CSP). This would give confidence to cloud users and enable them to choose the right CSP for their data-related interaction. This research proposes a solution, whereby the Trustworthiness of CSPs is calculated based on their Compliance with data security controls, User Feedback, and Auditor Rating. Two additional factors, Accuracy of Compliance Measurement and Control Significance Factor have been built in, to cater for other nonstandard conditions. Our implementation of Data Security Compliance Monitor and Data Trust as a Service, along with three CSPs, each with ten different settings, has supported our proposition through the devised formula. Experimental outcomes show changes in the trustworthiness value with changes in compliance level, user feedback and auditor rating. CSPs with better compliance have better trustworthiness values. However, if the Accuracy of Compliance Measurement and Control Significance Factor are low the trustworthiness is also proportionately less. This creates a balance and realism in our calculations. This model is unique and will help in creating users’ trust in cloud-based digital twins.
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