A Cloud Service Trust Evaluation Model Based on Combining Weights and Gray Correlation Analysis

Wang, Yubiao; Wen, Junhao; Wang, Xibin; Tao, Bamei; Zhou, Wei

doi:10.1155/2019/2437062

Cited by 34 publications

(19 citation statements)

References 22 publications

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“…Cloud computing services user satisfaction depends on the extent to which users can trust their cloud providers [54,55]. A cloud computing provider needs to build the trust of users by providing excellence in technical, business, and security areas [42].…”

Section: The Impact Of Having Trust In Cloud Providers Has On User Samentioning

confidence: 99%

Enhancing User Experiences with Cloud Computing via Improving Utilitarian and Hedonic Factors

et al. 2021

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This study provides insights into the initial and post-adoption of cloud computing services by integrating information technology adoption, social influence, trust, security, and information systems quality theories. Social influence, hedonicity, and automaticity are hedonic predictors of user satisfaction with cloud computing services. Perceived risks, trust in the provider, and system quality are utilitarian predictors of user satisfaction with cloud computing services. The Partial Least Squares (PLS) was employed to test eight hypotheses on the causal relationships between the variables. Six out of eight hypotheses were supported. Hedonic factors appear to have more influence than the utilitarian factor of increasing user satisfaction with cloud computing services in the school setting. The findings lead to both theoretical and practical implications for improving the initial and post-adoption of cloud computing services.

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Section: The Impact Of Having Trust In Cloud Providers Has On User Samentioning

confidence: 99%

Enhancing User Experiences with Cloud Computing via Improving Utilitarian and Hedonic Factors

et al. 2021

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“…For cloud computing environments, Wang et al 12 proposed a cloud service trust evaluation model based on weights and gray relation analysis, which combines rough set theory and the analytic hierarchy process to calculate direct trust and uses gray relation analysis to calculate the similarity of the recommendation trust. Al‐khafajiy et al 13 proposed a trust management method based on the service quality and protection of the historical quality, which improves security by using trust to identify and isolate malicious nodes.…”

Section: Related Workmentioning

confidence: 99%

A trust management model based on mutual trust and a reward‐with‐punishment mechanism for cloud environments

Guo

Yang

Luan

et al. 2021

Concurrency and Computation

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Aiming at overcoming problems such as malicious entities and trust crises in cloud environments, a trust management model based on mutual trust and a rewardwith-punishment mechanism is proposed in this paper. First, according to reputation values of entities and trust relationships among entities, we calculate the comprehensive trust values of a service request user to several candidate service providers and probe these providers with high comprehensive trust values. Second, we select a trade provider according to mutual trust. Finally, after the trade, we update the reputation values and other data of the request user, trade provider, and recommenders based on the final trade result to reward or punish them to various degrees, thus ultimately reducing malicious or dishonest behavior. The experimental results show that our model can effectively identify malicious entities to increase the trade success rate. K E Y W O R D Scloud environment, mutual trust, reputation, reward-with-punishment INTRODUCTIONCloud computing is a new network application model that was proposed by Google in 2006. Cloud computing was developed based on a series of traditional technologies, such as grid computing, distributed systems, peer to peer (P2P), and virtualization. It is a new information technology service resource. It distributes computing tasks over a resource pool that is composed of many computers, thereby enabling various application systems to obtain computing power, storage space, and information services as needed and providing dynamically scalable and cheap computing services on demand through the network. 1,2 Cloud computing can be divided into the following three categories according to the service model: IaaS (infrastructure as a service), PaaS (platform as a service), and SaaS (software as a service). 3 However, the widespread application of cloud computing technology also faces many security threats, such as privacy theft, resource misappropriation, virus attacks, and user malicious behavior attacks. The cloud environment is open, and users can add, delete, modify, and check data through the network. If users who passed the identity authentication behave maliciously, a major challenge will be posed to cloud computing security. Therefore, each user's behavior must be evaluated to identify the malicious users.In addition, there is a crisis of trust between cloud service providers and users: Users have doubts regarding whether cloud service providers can provide qualified cloud services in trade (in this paper, when a user buys and uses a provider's service, we say there is a εtradeε between the user and the provider, and the provider is called the trade provider.), while providers also have doubts about users. Therefore, to ensure the smooth operation of trades, the trust relationship between users and providers must be evaluated. Although researchers have proposed various trust models, most models consider only the attitude of user in the trade and ignore that of provider, or do not consider the impact of trade prices ...

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“…Trust is established by calculating the performance metrics, service ranking of the cloud service provider. Trust is also calculated by checking the recoverability from security breaches, service efficiency , and many more significant criteria [107]. The relationship between trustor and trustee plays a significant role when evaluating trust [108].…”

Section: Cloud Trust Model Based On Performancementioning

confidence: 99%

Untitled

2021

JESTR

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The emergence of the cloud computing paradigm has influenced business organizations for migration over two decades. This has enabled a more incredible communication speed paired with a better fault tolerance of web-based applications and the scope of having practically unlimited storage available for the user and business data. Such colossal data availability over cloud platforms has increased concerns about the security of the same from various threats such as unauthorized eavesdropping, deliberate malicious mutation of data, loss of data integrity, and identity theft. The presence of such security vulnerabilities has rendered cloud security to be an active research area. This article aims to deep dive into the present stateof-the-art in cloud security research and mitigation approaches. It is observed that cloud storage, cloud services are the widely used applications among the organization. However, vulnerabilities and security challenges faced by this two area is maximum. Besides, outsourcing sensitive information to cloud service provider's (CSP) end leads to trust issues among organizations. This article performs a detailed comparative analysis of the security frameworks developed towards cloud storage level, cloud service level, and cloud trust level to date and throws light on future research directives. All the existing research has been evaluated against CIA (confidentiality, Integrity, Availability) principles here.

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A Cloud Service Trust Evaluation Model Based on Combining Weights and Gray Correlation Analysis

Cited by 34 publications

References 22 publications

Enhancing User Experiences with Cloud Computing via Improving Utilitarian and Hedonic Factors

Enhancing User Experiences with Cloud Computing via Improving Utilitarian and Hedonic Factors

A trust management model based on mutual trust and a reward‐with‐punishment mechanism for cloud environments

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