Title: Cloud Computing And Confidentiality
Master of Science Graduation Thesis Computer Science
Project Intro: To explain the definition in short, “convenient on-demand network access”, together with “minimal management effort or service provider interaction,” stands for easy and fast network access to resources that are ready to use. With a “shared pool of resources,” the available computing resources of a cloud provider are combined as one big collection, to serve all users. The “rapid provisioning and releasing” of computing resources is used to quickly match available resources, with the need for those resources. This rapid provisioning prevents a lack of computing power when the need increases, while rapid release of assigned resources prevents that resources are idle while they may be required elsewhere.
Abstract: Cloud computing is an upcoming paradigm that offers tremendous advantages in economical aspects, such as reduced time to market, flexible computing capabilities, and limitless computing power. To use the full potential of cloud computing, data is transferred, processed and stored by external cloud providers. However, data owners are very skeptical to place their data outside their own control sphere. This thesis discusses to which degree this skepticism is justified, by presenting the Cloud Computing Confidentiality Framework (CCCF). The CCCF is a step-by-step framework that creates mapping from data sensitivity onto the most suitable cloud computing architecture. To achieve this, the CCCF determines first of all the security mechanisms required for each data sensitivity level, secondly which of these security controls may not be supported in certain computing environments, and finally which solutions can be used to cope with the identified security limitations of cloud computing. The most thorough security controls needed to protect the most sensitive data may not be guaranteed in public cloud computing architectures, while they can be realized in private cloud computing architectures. As the most promising cloud computing approach, this thesis suggests selective cloudbursting, which acts as a hybrid cloud model with selective data transfers between public and private clouds.
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