Efficient Cloud Storage Optimization Using Content-Defined Data Deduplication for Library Biography Systems

The exponential growth of digital biographical archives in cloud-based library systems poses significant challenges in storage efficiency, retrieval speed, and bandwidth utilization. This paper proposes a content-defined chunking (CDC) based data deduplication framework tailored for library biography systems, where multiple versions of biographies, images, and associated metadata often contain redundant data across different entries. The proposed system eliminates duplicate chunks at the sub-file level while preserving data integrity and enabling fast reconstruction. Experimental evaluation on a dataset of 10,000 biographical records (including text, scanned documents, and portraits) shows a storage saving of 68.5%, a bandwidth reduction of 72% during cloud synchronization, and a deduplication latency of under 2 seconds per record. The framework integrates seamlessly with cloud object storage (e.g., AWS S3) and maintains cryptographic hashing (SHA-256) for collision resistance. Results demonstrate that content-aware deduplication significantly outperforms fixed-size chunking and full-file hashing, making it highly suitable for evolving digital library ecosystems.