Decentralized Big Data Auditing Scheme for Cloud Storage Based on Blockchain with Adaptive EI-GAMAL and Gazelle Optimization
Showri Rayalu Bandanadam, R Prasanna Kumar · Journal of Computational and Cognitive Engineering · 2025
Cloud service providers may accidentally damage or delete user data in cloud storage, leading to data loss without user notification. To mitigate this, public auditing mechanisms are becoming increasingly crucial. However, many existing systems rely on third-party auditors (TPAs), which provide efficiency and fairness but remain vulnerable to malicious behavior. This risk stems from reliance on a centralized third party, highlighting the need for more secure methods. Blockchain technology offers a viable solution to mitigate this risk. By decentralizing the auditing process, blockchain eliminates reliance on a TPA, ensuring that data integrity validation is distributed and secure. Blockchain's transparency and immutability make it ideal for strengthening data auditing in cloud storage. In this enhanced auditing approach, cloud providers collaborate to validate data, establishing a decentralized framework. The process begins with the collection of data from traditional databases and its division into blocks for encryption. The adaptive EI-GAMAL algorithm, enhanced by the Enhanced Predator Success Rate of Gazelle Optimization, encrypts the data. The encrypted blocks are then stored in the cloud using the divide and conquer table (D&CT) concept, ensuring continuous updates to the location and metadata associated with the data. Each block contains a file ID, user ID, file data, and version number, which updates upon data modification or deletion. The location table keeps track of the file's location, which is also updated during the D&CT operation. This mechanism safeguards sensitive data and ensures its integrity through decentralized auditing. The performance of this blockchain-based auditing approach is validated against traditional methods, demonstrating greater effectiveness and security. Received: 27 December 2024 | Revised: 20 March 2025 | Accepted: 3 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Showri Rayalu Bandanadam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Prasanna Kumar Rangarajan: Supervision, Project administration.