Pydipala, Laxmikanth and T, Murali Mohan (2022) Ensuring Integrity: A Fine-Grained Approach to Query Results Verification in Encrypted Cloud Data Design and Analysis. IJFANS INTERNATIONAL JOURNAL OF FOOD AND NUTRITIONAL, 11 (4). pp. 844-850. ISSN 2319 1775
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Abstract
The cloud sector has attracted an unprecedented amount of interest from a wide range of businesses, including those in the software, BPO, healthcare, education, and other industries, in the present day. Notwithstanding the considerable increase in the adoption of cloud computing, it is important to highlight the deficiencies of current cloud service providers in terms of providing comprehensive data privacy via encryption and message digest mechanisms for data authorization. It is worth noting that cloud servers may display a degree of deceit by intentionally excluding qualified outcomes in order to optimize computational resources and reduce communication latency. This article proposes and thoroughly examines a mechanism for verifying query results that is secure, readily integrable, and fine-grained, thereby addressing the critical privacy gap in cloud data. This innovative method permits users to determine data authorization and evaluate the quality of each data file within an encrypted query results set. The proposed methodology utilizes the widely recognized MD5/SHA1 algorithms for message digest in order to produce brief signature keys. These keys are of utmost importance in the process of authenticating data, guaranteeing that users can have confidence in the soundness of the results of their queries while safeguarding the privacy of sensitive data. This paper provides valuable insights into the efficacy and resilience of the suggested mechanism by conducting an exhaustive analysis. It thereby contributes to the ongoing discussion surrounding the improvement of privacy and security in cloud computing environments.
Item Type: | Article |
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Subjects: | E Computer Science and Engineering > E2 Cloud Computing E Computer Science and Engineering > E3 Artificial Intelligence and Machine Learning |
Departments: | Computer Science and Engineering |
Depositing User: | Dr Laxmikanth P |
Date Deposited: | 07 Mar 2024 10:13 |
Last Modified: | 07 Mar 2024 10:13 |
URI: | https://ir.vignanits.ac.in/id/eprint/266 |