Enhancing cloud data security: User-centric approaches and advanced mechanisms
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.1.29Keywords:
Cloud storage, Encryption algorithms, Biometric authentication, Cloud data security, Security validation, Authentication success rate.Dimensions 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.
Cloud storage has led to a transformative era of data management for organizations, but this paradigm shift has also introduced critical security challenges. This paper is motivated by the urgent need to strengthen cloud data security against unauthorized access and breaches. Our investigation revolves around the vulnerabilities stemming from distributed links in cloud storage, shedding light on the paramount importance of safeguarding crucial commercial records. These challenges encompass the menace posed by both external attackers and unscrupulous customers, amplified within the complex landscape of multi-tenant architectures. This work presents the Secure-Cloud-Guard algorithm to achieve these goals—a multifaceted approach integrating encryption, biometric authentication, and MAC address security mechanisms. The Secure-Cloud-Guard algorithm, rooted in the user-centric paradigm, enhances data protection in cloud storage environments by orchestrating multiple layers of security. The algorithm’s simulation involves thorough evaluation through encryption performance analysis, biometric authentication testing, and MAC address security validation. The simulation results reveal the effectiveness of our proposed algorithm. Encryption performance metrics showcase the encryption throughput and latency, thereby gauging the efficiency of the encryption process. The biometric authentication simulation calculates the false acceptance rate (FAR) to determine the algorithm’s accuracy and false rejection rate (FRR). The simulation of MAC address security illustrates the algorithm’s ability to authenticate devices through MAC addresses, providing insights into its authentication success rate (ASR). In conclusion, an, aligning with user-centric approaches and incorporating advanced mechanisms, such as encryption, biometric authentication, MAC address security, contribute to the ongoing efforts of fortifying the security landscape of cloud storage and computingAbstract
How to Cite
Downloads
Similar Articles
- Remya Raj B., R. Suganya, A novel and an effective intrusion detection system using machine learning techniques , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- S. Gomathi, C. Radhika, A secure messaging application using steganography and AES encryption a dual-layer secure messaging system , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Arunachalaprabu G, Fathima Bibi K, A pattern-driven Huffman encoding and positional encoding for DNA compression , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- Susithra N, Rajalakshmi K, Ashwath P, Performance analysis of compressive sensing and reconstruction by LASSO and OMP for audio signal processing applications , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- G. Vijayalakshmi, M. V. Srinath, Student’s Academic Performance Improvement Using Adaptive Ensemble Learning Method , The Scientific Temper: Vol. 16 No. 11 (2025): The Scientific Temper
- V. Baby Deepa, R. Jeya, Dynamic resource allocation with otpimization techniques for qos in cloud computing , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Amit Maru, Dhaval Vyas, Hybrid deep learning approach for pre-flood and post-flood classification of remote sensed data , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Nilesh M. Patil, P M. Krishna, G. Deena, C Harini, R.K. Gnanamurthy, Romala V. Srinivas, Exploring real-time patient monitoring and data analytics with IoT-based smart healthcare monitoring , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- S. Prabagar, Vinay K. Nassa, Senthil V. M, Shilpa Abhang, Pravin P. Adivarekar, Sridevi R, Python-based social science applications’ profiling and optimization on HPC systems using task and data parallelism , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Ellakkiya Mathanraj, Ravi N. Reddy, Enhanced principal component gradient round-robin load balancing in cloud computing , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
<< < 2 3 4 5 6 7 8 9 10 11 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- J. M. Aslam, K. M. Kumar, Enhancing security of cloud using static IP techniques , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper

