Integrating Blockchain with Cloud-Based Relational Databases for Decentralized Data Integrity and Enhanced Security

Abstract

The swift growth of cloud computing has fundamentally changed the way we process store and manage data. This is primarily because of platforms like Amazon Web Services (AWS) Google Cloud and Microsoft Azure which are well-known for their affordability scalability and flexibility. Even with these developments there are still many obstacles to overcome especially when it comes to protecting data security guaranteeing reliability and avoiding unwanted access—all of which are critical issues in the increasingly digital world where relational databases (RDBs) are essential for effectively connecting and organizing vast amounts of structured data. However issues like data tampering and restricted user control over data are common with traditional centralized systems for managing these databases underscoring the urgent need for creative solutions to improve the security and dependability of cloud-based relational databases. The proposed model uses mobile agent technology to deploy a network of distributed virtual machine agents which serve as the foundation for a blockchain-based system that protects data integrity by carrying out crucial tasks like secure data storage continuous monitoring and real-time verification while also facilitating smooth user collaboration. This study focuses on creating a decentralized system that combines blockchain technology with relational databases hosted in the cloud to improve data security and integrity. By placing a strong emphasis on decentralization security and user-controlled data management this framework not only solves the enduring problems of data integrity and trust in cloud environments but it also offers a safe and effective platform for information management in the quickly evolving digital world of today. It also shows how the combination of blockchain technology and cloud systems can revolutionize data management procedures. Future studies could also concentrate on enhancing this models scalability and computational efficiency which would enable it to be used for a wider variety of cloud computing applications. This would increase the models practical utility and broaden its potential to satisfy the various demands of contemporary cloud-based systems.