Enhanced Positional Vigenère (EPV): A Confidentiality-Enabled Encryption Technique for Secure Cloud Storage
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2026.17.4.09Keywords:
Cloud Computing, Data Security, Encryption, Enhanced Positional Vigenère (EPV)Dimensions Badge
Issue
Section
License
Copyright (c) 2026 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Cloud computing is an internet-based computing paradigm that provides various hosting and delivery services over the internet. It offers computational resources to users based on their demand. Data storage is one of the main benefits of cloud computing. It provides users with plenty of space to store their data neatly and access it easily, no fuss involved. More and more companies are jumping on cloud platforms that offer Storage as a Service (STaaS), helping them skip the hefty upfront costs and ongoing maintenance of their own servers. However, when organizational and enterprise data are moved to public cloud storage, ensuring data protection and security becomes a critical concern. If unencrypted data is transmitted to the public cloud, there is a possibility of data breaches during transmission. To address this issue, an efficient technique called Enhanced Positional Vigenère (EPV) is proposed in this work. Strengthening the ciphertext and improving data security are the goals of the suggested approach. The EPV technique is implemented in Java, and our experiments show it boosts performance, ramps up efficiency, and makes the ciphertext even more complex.Abstract
How to Cite
Downloads
Similar Articles
- Rekha R., P. Meenakshi Sundaram, Trust aware clustering approach for the detection of malicious nodes in the WSN , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Brigith Gladys L, Merline Vinotha J, Sustainable fuzzy rough multi-objective multi-route cold transportation model with traffic flow and route constraints , The Scientific Temper: Vol. 16 No. 01 (2025): The Scientific Temper
- Y. Mohammed Iqbal, M. Mohamed Surputheen, S. Peerbasha, A COVID Net-predictor: A multi-head CNN and LSTM-based deep learning framework for COVID-19 diagnosis , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- J. Fathima Fouzia, M. Mohamed Surputheen, M. Rajakumar, Hybrid pigeon optimization-based feature selection and modified multi-class semantic segmentation for skin cancer detection (HPO-MMSS) , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- Gautam Patil, Unnati Soni, Social Inequalities and Health Disparities among Scheduled Castes and Scheduled Tribes: A Gender and Income Perspective in Maharashtra , The Scientific Temper: Vol. 17 No. 03 (2026): The Scientific Temper
- Sivakumar S, Rajasekaran Kondareddy, Kalyani Ayyemperumal, Building SaaS solutions using microsoft azure for achieving safe and secure tax related software , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Chirag Darji, Rajesh Chauhan, Views of undergraduates on Vikshit Bharat@2047 , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Vaishali Yeole, Rushikesh Yeole, Pradheep Manisekaran, Analysis and prediction of stomach cancer using machine learning , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Rashmi Rani, ROLE OF NEUROTICISM AND EXTRAVERSION FACTORS OF PERSONALITY ON LIFE SATISFACTION IN MARRIED COUPLES , The Scientific Temper: Vol. 8 No. 1&2 (2017): The Scientific Temper
- Ahmed Mustefa, Validating the dairy marketing performance of Mizan-Aman town, Bench-Sheko zone, Ethiopia , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
<< < 38 39 40 41 42 43 44 45 46 47 > >>
You may also start an advanced similarity search for this article.

