ETTG: Enhanced token and tag generation for authenticating users and deduplicating data stored in public cloud storage
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.3.18Keywords:
Cloud Storage, Data Deduplication, Token Generation, Cryptographic Techniques, Computation EfficiencyDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
As cloud storage services continue to grow in popularity, the need for secure and efficient data management has become paramount. Public cloud storage offers benefits such as cost efficiency, scalability, and accessibility, but it also presents significant challenges related to data security and storage optimization. To address these challenges, the paper proposes an Enhanced Token and Tag Generation (ETTG) technique designed to improve data deduplication in public cloud storage. ETTG utilizes advanced cryptographic methods to generate secure tokens and tags, ensuring robust, efficient deduplication processes. The comprehensive evaluation demonstrates that ETTG significantly reduces computation time compared to existing techniques, making it particularly suitable for data-intensive cloud environments. By minimizing redundant data and enhancing data security, ETTG not only optimizes storage utilization but also improves overall system performance. This paper details the design and implementation of ETTG, its evaluation against existing methods, and its potential impact on the efficiency and security of cloud storage services.Abstract
How to Cite
Downloads
Similar Articles
- Rahat Yezdani, S. M. K. Quadri, A PPR-based energy-efficient VM consolidation in cloud computing , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- A. Jabeen, AR Mohamed Shanavas, Bradley Terry Brownboost and Lemke flower pollinated resource efficient task scheduling in cloud computing , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- D. Jayadurga, A. Chandrabose, Distribution of virtual machines with SVM-FFDM approach in cloud computing , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- S. Vanaja, Hari Ganesh S, Application of data mining and machine learning approaches in the prediction of heart disease – A literature survey , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- K. Mohamed Arif Khan, A.R. Mohamed Shanavas, Optimizing IoT application deployment with fog - cloud paradigm: A resource-aware approach , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Chandrasekaran M, Rajesh P K, Optimization of cost to customer of power train in commercial vehicle using knapsack dynamic programming influenced by vehicle IoT data , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- A. Kalaiselvi, A. Chandrabose, Fuzzy logic-driven scheduling for cloud computing operations: a dynamic and adaptive approach , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- P. S. Dheepika, V. Umadevi, An optimized approach for detection and mitigation of DDoS attack cloud using an ensembled deep learning approach , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- S. C. Prabha, P. Sivaraaj, S. Kantha Lakshmi, Data analysis and machine learning-based modeling for real-time production , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Maria D. Roopa, Nimitha John, Bayesian Optimization Phase I Design of Experiment Models , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Olivia C. Gold, Jayasimman Lawrence, Ensemble of CatBoost and neural networks with hybrid feature selection for enhanced heart disease prediction , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper

