Survey of Various Data Deduplication Method and Security Algorithm Issue in Cloud Computing
Keywords:
Cloud Computing, Data De-duplication, TF-IDF, Inverted Index, Cloud Storage, Information Retrieval, Precision, Recall, Storage Optimization, Search Performance.Abstract
Cloud computing has become a widely adopted platform for large-scale data storage and management. However, the rapid growth of data has led to the problem of duplicate data storage, which increases storage requirements, reduces server efficiency, and degrades overall system performance. This paper proposes a de-duplication framework that enhances cloud storage performance by identifying and eliminating redundant data before it is stored on the server. The proposed approach employs the Term Frequency–Inverse Document Frequency (TF-IDF) technique for feature extraction and an inverted index for efficient duplicate detection and retrieval. By storing only unique data instances, the system optimizes storage utilization, reduces redundancy, and improves search efficiency. Experimental results demonstrate that the proposed method improves server performance by reducing de-duplication time, increasing storage efficiency, and enhancing search accuracy in terms of precision and recall. The proposed framework provides an effective solution for cloud data management and can be extended in future work by integrating advanced de-duplication techniques with secure data transmission and encryption mechanisms to further improve security and time efficiency.
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