Comparative Analysis of the Centralized and Decentralized Architecture of Cloud Computing in terms of Privacy Security

Yilin Chen

doi:10.54254/2755-2721/2025.21867

Research Article

Open access

Published on 7 April 2025

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Chen,Y. (2025). Comparative Analysis of the Centralized and Decentralized Architecture of Cloud Computing in terms of Privacy Security. Applied and Computational Engineering,145,51-56.

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Comparative Analysis of the Centralized and Decentralized Architecture of Cloud Computing in terms of Privacy Security

Yilin Chen *^,1,

¹ Beijing-Dublin International College, Beijing University of Technology, Beijing, China, 100124

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2025.21867

Abstract

In an era of rapid technological evolution, cloud computing has become indispensable across various industries due to its cost-efficiency, scalability, and accessibility. Yet, privacy and security concerns persist, as sensitive data can be susceptible to breaches and unauthorized access. At present, the two mainstream cloud computing architectures are centralized architecture and decentralized architecture. Both have advantages and disadvantages in terms of confidentiality, integrity and data availability. This paper employs a comparative analysis of these two architectures, synthesizing insights from recent studies and emphasizing the potential of emerging technologies like blockchain to strengthen privacy protection. By examining both architectures through a high-level lens, this study explores how decentralization, supported by distributed consensus mechanisms, can address vulnerabilities and enhance trust among stakeholders. It can be concluded that a decentralized approach, when underpinned by robust cryptographic methods, offers superior safeguards against evolving threats.

Keywords

centralized, decentralized, privacy, cloud computing

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Cite this article

Chen,Y. (2025). Comparative Analysis of the Centralized and Decentralized Architecture of Cloud Computing in terms of Privacy Security. Applied and Computational Engineering,145,51-56.

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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning

Conference website: https://2025.confseml.org/

ISBN：978-1-80590-024-5(Print) / 978-1-80590-023-8(Online)

Conference date: 2 July 2025

Editor：Marwan Omar

Series: Applied and Computational Engineering

Volume number: Vol.145

ISSN：2755-2721(Print) / 2755-273X(Online)

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