Pseudonym-Based Privacy Preserving Framework for Facilitating Cloud Data Security
Premila Rosy
Premila Rosy, Research Scholar, Bharathiyar University, Coimbatore, (Tamil Nadu), India.
Manuscript received on 20 March 2019 | Revised Manuscript received on 25 March 2019 | Manuscript published on 30 July 2019 | PP: 6516-6526 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2514078219/2019©BEIESP | DOI: 10.35940/ijrte.B2514.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The advent of cloud computing has revolutionized the option of sharing cloud resources among the cloud users for minimizing the cost overhead. But, the cloud data security is considered as the predominant issue that need to be addressed through the implementation of privacy preserving approaches that sustains and prevents the cloud resources and users from being compromised by the malicious intruders. In this paper, a Pseudonym-based Privacy Preservation Framework (PBPRF) is proposed for understanding its potential towards the accuracy and privacy preservation of cloud data based on the concept of P-Gene. This proposed PBPRF incorporates the benefits of the P-Gene which is responsible in the cloud space for providing security for the stored and utilized private data in the cloud that are periodically exchanged with the clients of the cloud environment. This proposed PBPRF scheme ensures secure sharing of data by relying on a trustworthy data aggregation scheme which is fully dependent on erasable data hiding technique. The simulation experiments and results of the proposed PBPRF mechanism is compared with the baseline PRIAS and TPAAAS techniques in terms of pseudonym generation cost, pseudonym verification, execution time and pseudonym verification time under batched and separated environment.
Keywords: PRIAS and TPAAAS Techniques in terms of Pseudonym Generation Cost,
Scope of the Article: Patterns and Frameworks