Blockchain for Library Records Management: A Secure and Decentralized Approach - Sharma, Shweta; Lakshmanan, L.; Sahu, Prabhat Kumar; Agarwal, Trapty; Tejeshwari, M. R (Indian Journal of Information Sources and Services, 2025)
Aim: In recent times, growths in digital infrastructure have led to the rapid expansion of Electronic Library Records (ELRs). An active Library Records Management (LRM) information system allows data owners’, students, faculty, and staff, to achieve and strongly share their data with chosen entities. However, the rising volume of ELRs poses challenges in ensuring data security, access control, and efficient resource usage. Methods: To address these difficulties, this research presents a novel Blockchain Enabled Secure Library Records Management (BESLRM) with a Deep Learning (DL) model. The construction allows library administrators to read and update records safely, gives users-controlled access privileges, and facilitates automated alerts for overdue returns or the accessibility of reserved books. For data encryption, the Advanced Encryption Standard (AES) is employed, while the Efficient Transient Search Optimization Algorithm (ETSO) is used to produce optimal encryption keys, enhancing the AES performance. Library data is collected from user logs, and borrow records. Preprocessing involves cleaning, encoding, and normalization. Block chain records user actions like reservations, returns, and book check-outs, and it is used to store and trade library data. Digital resources or external data are safely connected to external storage. An Efficient Transient Search-driven Conditional Variational Autoencoder (ETS-CVAE) is used to forecast user preferences, identify unusual usage patterns, and optimize inventory after decryption at the authorized end. Results: The suggested approach exhibits better performance, which includes a recall of 96.50%, accuracy of 98%, F1-score of 95.89%, precision of 94%, and Kappa score of 93%. Conclusion: Experimental conclusions confirm that the proposed model confirms superior safety, transparency, and efficiency compared to traditional LRM approaches.