A Hybrid B-Tree and Enhanced Merkle Hash Tree Approach for Secure Cloud Data Management

Data security, integrity verification, and efficient retrieval issues are much more pronounced with large datasets in cloud storage systems. The angle of this paper is the proposal of hybrid architecture of AES, EMHT, B-tree indexing, and POR techniques to complete the optimization of cloud operations. The way the system was evaluated against the traditional approaches was based on several key metrics such as encryption time, integrity verification time, update time, and memory usage for datasets up to the 15GB limit. The experimental results show that the proposed system has 6.50% less verification time and 38.44% better update time when compared to existing methods without compromising the effectiveness of encryption. These indicate that the proposed approach is cost-effective, secure, and scalable in a large-scale cloud storage environment. The contribution of this paper is a practical solution for future enhanced data management and security in modern cloud systems.