Adaptive and Scalable Cloud Data Sharing Framework with Quantum-Resistant Security, Decentralized Auditing, and Machine Learning-Based Threat Detection

The increasing prevalence of cloud environments makes it important to ensure secure and efficient data sharing between dynamic teams, especially in terms of user access and termination based on proxy re-encryption and hybrid authentication management schemes aimed at increasing scalability, flexibility, and adaptability and exploring a multi-proxy server architecture to distribute re-encryption tasks, improve fault tolerance and load balancing in large deployments. In addition, to this eliminated the need for trusted third-party auditors, integrate blockchain-based audit mechanisms for immutable decentralized monitoring of data access, revocation events To future-proof systems provides quantum-resistant cryptographic mechanisms for long-term security as well as to develop revolutionary approaches that drive the user out of the box, driven by machine learning to predict and execute addressing potential threats in real-time. Proposed systems also introduce fine-grained, multi-level access controls for discrete data security and privacy, meeting different roles of users and data sensitivity levels mean improvements greater in terms of computing performance, security and scalability, making this enhanced system more effective for secure data sharing at dynamic and large clouds around us.

@article{Reddy2025,
  title     = {Adaptive and Scalable Cloud Data Sharing Framework with Quantum-Resistant Security, Decentralized Auditing, and Machine Learning-Based Threat Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {P Raja Sekhar Reddy and Pulipati Srilatha and Kanhaiya Sharma and Sudipta Banerjee and Shailaja Salagrama and Manjusha Tomar and Ashwin Tomar},
  doi       = {10.14569/IJACSA.2025.01602103},
  url       = {https://doi.org/10.14569/IJACSA.2025.01602103}
}