Privacy-enabled academic certificate authentication and deep learning-based student performance prediction system using hyperledger blockchain technology

• Published in: Journal of parallel and distributed computing Vol. 204; p. 105119
• Main Authors: A․S, Sangeetha, S, Shunmugan
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2025
• Subjects: Authentication; Blockchain; Certificate verification; Hashing function; XOR; XOR; Hashing function; Certificate verification; Authentication; Blockchain
• ISSN: 0743-7315
• DOI: 10.1016/j.jpdc.2025.105119

•Various entities, like student, system, university, BC, and company are considered.•Authentication is performed on the basis of public key, private key and secret keys.•XOR and hashing functions are used to enhance security. Blockchain systems do not rely on trust for electronic transactions and it emerged as a popular technology due to its attributes like immutability, transparency, distributed storage, and decentralized control. Student certificates and skill verification play crucial roles in job applications and other purposes. In traditional systems, certificate forgery is a common problem, especially in online education. Processes, such as issuing and verifying student certifications along with student performance prediction for higher education or job recruitment are often lengthy and time-consuming. Integrating blockchain into certificate verification protocols offers authenticity and significantly reduces processing times. Hence, this research introduced a novel secure privacy preservation-based academic certificate authentication system (CertAuthSystem) for verifying the academic certificates of students. The CertAuthSystem contains different entities, such as Student, System, University, Blockchain, and Company. The university issues certificates to students, which are stored in Blockchain, and when the student applies for a job/scholarship, he/she transmits the certificate and the blockID to the organization, based on which verification is performed. Moreover, the student’s performance is predicted by a classifier named Deep Long Short-Term Memory (DLSTM). Then, CertAuthSystem is examined for its superiority considering measures, like validation time, memory, throughput and execution time and has achieved values of 53.412 ms, 86.6 MB, 94.876 Mbps, and 73.57 ms, correspondingly for block size 7. Finally, the prediction analysis of the DLSTM classifier is done based on evaluation metrics, such as precision, recall and F measure, which attained superior values of 90.77 %, 92.99 %, and 91.86 %.