A trust evaluation model for selecting service provider in cloud environment

Agheli, Nafiseh; Hosseini, Bahare; Shojaee, Aida

doi:10.1109/iccke.2014.6993418

Cited by 7 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Agheli et al proposed a trust evaluation model for selecting the CSPs in the cloud environments. Their model was based on satisfied feedback ratings and risk factor, enabling the CSP to increase their CS quality through an intact competition . A context‐aware multifaceted trust framework named CAMTF, presented by Alhanahnah et al, assists in evaluating the trust of CSPs.…”

Section: Literature Reviewmentioning

confidence: 99%

MMLT: A mutual multilevel trust framework based on trusted third parties in multicloud environments

et al. 2020

View full text Add to dashboard Cite

In this article, a mutual multilevel trust framework is proposed, which involves managing trust from the perspective of cloud users (CUs) and cloud service providers (CSPs) in a multicloud environment based on a set of trusted third parties (TTPs). These independent agents are trusted by CUs and CSPs and distributed on different clouds. The TTPs evaluate the CUs' trustworthiness based on the accuracy of feedback ratings and assess the CSPs' trustworthiness based on the quality of service monitoring information. They are connected themselves through the trusted release network, which enables a TTP to obtain trust information about CSPs and CUs from other clouds. With the objective of developing an effective trust management framework, a new approach has been provided to improve trust-based interactions, that is, able to rank the trusted cloud services (CSs) based on CU's priorities via fuzzy logic. Fuzzy logic is applied to manage the different priorities of CUs, all the CUs do not have the same priorities to use trusted CSs. Customizing service ranking allows CUs to apply trusted CSs based on their priorities. Experiments on real datasets well matched the analytical results, indicating that our proposed approach is effective and outperforms the existing approaches.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

MMLT: A mutual multilevel trust framework based on trusted third parties in multicloud environments

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In [11] expressed a subjective logic trust model based to combine the factors like direct experiences, satisfied interaction feedback, reputation values, and risk factor. The method addressed the problem of creating trust in the cloud services and its evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

Resource Allocation Based on Matchmaking Services in Multiple Clouds Using Trustworthy and Scalable Service Providers Algorithm

Solainayagi¹,

Ponnusamy²

2019

IJIES

View full text Add to dashboard Cite

Nowadays, cloud server adoption becomes more popular and highly demanded due to unlimited data contribution and retrieval from anywhere and anytime. Existing methods have lacks of adaptability issues with a trust value calculation based on multi-dimensional cloud service providers. Some, existing methods believed in expert opinion to evaluate the trust factors. However, the techniques have many adaptability issues and trust evaluation results have many errors. To bring the better solution, Trustworthy and Scalable Service Providers Algorithm is proposed for analyzing the design the relationship among the users, the broker, and cloud service providers. The proposed method works for resource allocation based on matchmaking service among multiple clouds. The trust-based proposed method efficiently minimizes the cloud user burden and enhances the system stabilities. Proposed method works based on information entropy theory to evaluate the multi-attribute based decision-making. Here, cloud user efficiently can find trustable cloud service providers in advance. Where, cloud service providers are more dependent on cloud users. Based on the excremental results, the proposed method reduces the System execution time 2 milliseconds; communication cost 9.33 % and improves the 39.33 % Trust score compare than existing methodologies in multiple cloud environments.

show abstract