Enhanced mechanism to prioritize the cloud data privacy factors using AHP and TOPSIS: a hybrid approach

Mohammad Zunnun Khan, Mohd Shoaib, Mohd Shahid Husain, Khair Ul Nisa, Mohammad. Tabrez Quasim
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Abstract

Cloud computing is a new paradigm in this new cyber era. Nowadays, most organizations are showing more reliability in this environment. The increasing reliability of the Cloud also makes it vulnerable. As vulnerability increases, there will be a greater need for privacy in terms of data, and utilizing secure services is highly recommended. So, data on the Cloud must have some privacy mechanisms to ensure personal and organizational privacy. So, for this, we must have an authentic way to increase the trust and reliability of the organization and individuals The authors have tried to create a way to rank things that uses the Analytical Hieratical Process (AHP) and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Based on the result and comparison, produce some hidden advantages named cost, benefit, risk and opportunity-based outcomes of the result. In this paper, we are developing a cloud data privacy model; for this, we have done an intensive literature review by including Privacy factors such as Access Control, Authentication, Authorization, Trustworthiness, Confidentiality, Integrity, and Availability. Based on that review, we have chosen a few parameters that affect cloud data privacy in all the phases of the data life cycle. Most of the already available methods must be revised per the industry’s current trends. Here, we will use Analytical Hieratical Process and Technique for Order Preference by Similarity to the Ideal Solution method to prove that our claim is better than other cloud data privacy models. In this paper, the author has selected the weights of the individual cloud data privacy criteria and further calculated the rank of individual data privacy criteria using the AHP method and subsequently utilized the final weights as input of the TOPSIS method to rank the cloud data privacy criteria.
使用 AHP 和 TOPSIS 对云数据隐私因素进行优先排序的增强机制:一种混合方法
云计算是新网络时代的一种新模式。如今,大多数组织在这一环境中表现出更高的可靠性。云计算可靠性的提高也使其变得脆弱。随着脆弱性的增加,对数据隐私的需求也会增加,因此强烈建议使用安全的服务。因此,云上的数据必须有一些隐私机制,以确保个人和组织的隐私。为此,我们必须有一种真实的方式来提高组织和个人的信任度和可靠性。作者尝试创建一种排序方式,使用层次分析法(AHP)和与理想解决方案相似的排序偏好技术(TOPSIS)。在结果和比较的基础上,产生一些隐藏的优势,即基于成本、效益、风险和机会的结果。在本文中,我们正在开发一个云数据隐私模型;为此,我们进行了深入的文献综述,包括访问控制、身份验证、授权、可信度、保密性、完整性和可用性等隐私因素。在此基础上,我们选择了在数据生命周期的所有阶段影响云数据隐私的几个参数。大多数现有方法都必须根据行业当前趋势进行修订。在此,我们将使用分析层次过程和通过与理想解决方案相似性进行排序偏好的技术方法来证明我们的主张优于其他云数据隐私模型。在本文中,作者选择了各个云数据隐私标准的权重,并使用 AHP 方法进一步计算了各个数据隐私标准的排序,随后利用最终权重作为 TOPSIS 方法的输入,对云数据隐私标准进行排序。
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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