Please use this identifier to cite or link to this item: https://zone.biblio.laurentian.ca/handle/10219/4059
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dc.contributor.authorPeng, Chengyu-
dc.date.accessioned2023-06-16T13:02:46Z-
dc.date.available2023-06-16T13:02:46Z-
dc.date.issued2023-04-19-
dc.identifier.urihttps://zone.biblio.laurentian.ca/handle/10219/4059-
dc.description.abstractWith the rise in popularity of cloud computing, there is a growing trend toward the storage of data in a cloud environment. However, there is a significant increase in the risk of privacy information leakage, and users could face serious challenges as a result of data leakage. In this paper, we propose an allocation scheme for the storage of data in a collaborative edge-cloud environment, with a focus on enhanced data privacy. In addition, we explore an extended application of the approach to sourcing. Specifically, we first evaluate the datasets and servers. We then introduce several constraints and use the Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model to formalize the problem. Based on the qualification value, we can find the optimal allocation using the IBM ILOG CPLEX Optimization (CPLEX) Package. At a given scale, the allocation scheme scores based on our method improve by about 50% compared to the baseline method and the trust-based method. Moreover, we use a similar approach to analyze procurement issues in the supply chain to help companies reduce the carbon emissions. This shows that our proposed solution can store data in servers that better suit their requirements and is adaptable to other problems.en_US
dc.language.isoenen_US
dc.subjectAllocationen_US
dc.subjectstrategyen_US
dc.subjectcollaborationen_US
dc.subjectE-CARGOen_US
dc.subjectGroup Role Assignment (GRA)en_US
dc.subjectprivacyen_US
dc.subjectgreen computingen_US
dc.titleOptimal data allocation method considering privacy enhancement using E-CARGOen_US
dc.typeThesisen_US
dc.description.degreeMaster of Science (MSc) in Computational Sciencesen_US
dc.publisher.grantorLaurentian University of Sudburyen_US
Appears in Collections:Computational Sciences - Master's theses

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