The Cloud computing paradigm provides numerous attractive services to customers such as the provision of the on-demand self-service, usage-based pricing, ubiquitous network access, transference of risk, and location independent resource sharing. However, the security of cloud computing, especially its data privacy, is a highly challengeable task. To address the data privacy issues, several mechanisms have been proposed that use the third party auditor (TPA) to ensure the integrity of outsourced data for the satisfaction of cloud users (CUs). However, the role of the TPA could be the potential security threat itself and can create new security vulnerabilities for the customer’s data. Moreover, the cloud service providers (CSPs) and the CUs could also be the adversaries while deteriorating the stored private data. As a result, the objective of this research is twofold. Our first research goal is to analyze the data privacy-preserving issues by identifying unique privacy requirements and presenting a supportable solution that eliminates the possible threats towards data privacy. Our second research goal is to develop the privacy-preserving model (PPM) to audit all the stakeholders in order to provide a relatively secure cloud computing environment. Specifically, the proposed model ensures the quality of service (QoS) of cloud services and detects potential malicious insiders in CSPs and TPAs. Furthermore, our proposed model provides a methodology to audit a TPA for minimizing any potential insider threats. In addition, CUs can use the proposed model to periodically audit the CSPs using the TPA to ensure the integrity of the outsourced data. For demonstrating and validating the performance, the proposed PPM is programmed in C++ and tested on GreenCloud with NS2 by applying merging processes. The experimental results help to identify the effectiveness, operational efficiency, and reliability of the CSPs. In addition, the results demonstrate the successful rate of handling the negative role of the TPA and determining the TPA’s malicious insider detection capabilities.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications