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@article{207872,
        author = {Suvarna Lahanu Ghogare},
        title = {Byte Level Deduplication},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {3194-3201},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207872},
        abstract = {Byte-level deduplication is an advanced data optimization technique used to eliminate redundant data at the finest granularity by identifying and removing duplicate byte sequences. Unlike file-level or block-level deduplication, this method analyzes data at the byte level, enabling higher storage efficiency by detecting even the smallest repetitions within and across files. The process typically involves breaking data into variable-length segments, generating unique fingerprints using hashing algorithms, and storing only unique byte patterns while maintaining references to duplicates.This approach significantly reduces storage requirements, improves bandwidth utilization during data transfer, and enhances backup and archival performance. Byte-level deduplication is widely applied in cloud storage systems, distributed file systems, and backup solutions where large volumes of repetitive data are common. However, the technique introduces computational overhead and complexity in indexing and retrieval due to its fine granularity. Despite these challenges, byte-level deduplication remains a powerful solution for efficient data management in modern large-scale storage environments.},
        keywords = {Byte-Level Deduplication, Data Deduplication, Data Redundancy, Variable-Length Segmentation, Data Fingerprinting, Hashing Algorithms, Storage Optimization, Cloud Storage, Distributed File Systems, Backup and Archival Systems, Bandwidth Optimization, Storage Efficiency.},
        month = {August},
        }