A Blockchain Hyperledger and Non-Linear Machine Learning: A Novel and Secure Educational Accreditation Registration and Distributed Ledger Preservation Architecture

Shaikh Z.A. Khan A.A. Baitenova L. Zambinova G. Yegina N. Ivolgina N. Laghari A.A. Barykin S.E.
March-1 2022 MDPI

Applied Sciences (Switzerland)
2022 #12 Issue 5

This paper proposes a novel and secure blockchain hyperledger sawtooth-enabled consortium analytical model for smart educational accreditation credential evaluation. Indeed, candidate academic credentials are generated, verified, and validated by the universities and transmitted to the Higher Education Department (HED). The objective is to enable the procedure of credential verification and analyze tamper-proof forged records before validation. For this reason, we designed and created an accreditation analytical model to investigate individual collected credentials from universities and examine candidates’ records of credibility using machine learning techniques and maintain all these aspects of analysis and addresses in the distributed storage with a secure hash-encryption (SHA-256) blockchain consortium network, which runs on a peer-to-peer (P2P) structure. In this proposed analytical model, we deployed a blockchain distributed mechanism to investigate the examiner and analyst processes of accreditation credential protection and storage criteria, which are referred to as chaincodes or smart contracts. These chaincodes automate the distributed credential schedule, generation, verification, validation, and monitoring of the overall model nodes’ transac-tions. The chaincodes include candidate registration with the associated university (candidateReg()), certificate-related accreditation credentials update (CIssuanceTrans()), and every node’s transactions preservation in the immutable storage (ULedgerAV()) for further investigations. This model simulates the educational benchmark dataset. The result shows the merit of our model. Through extensive sim-ulations, the blockchain-enabled analytical model provides robust performance in terms of credential management and accreditation credibility problems.

Artificial neural network , Blockchain , Certificate credentials accreditation , Consortium network , Hyperledger sawtooth , Machine learning

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Faculty of Computing Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Sindh, Karachi, 75660, Pakistan
Department of Computer Science, Sindh Madressatul Islam University, Sindh, Karachi, 74000, Pakistan
Almaty University of Power Engineering and Telecommunications (AUPET) Named after G.Daukeev, Almaty, 050013, Kazakhstan
Kazakh University of Economics, Finance and International Trade, Nur-Sultan, 010005, Kazakhstan
Department of Economics, Ogarev Mordovia State University, Saransk, 430005, Russian Federation
Plekhanov Russian University of Economics, Moscow, 115903, Russian Federation
Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, 195251, Russian Federation

Faculty of Computing Science and Information Technology
Department of Computer Science
Almaty University of Power Engineering and Telecommunications (AUPET) Named after G.Daukeev
Kazakh University of Economics
Department of Economics
Plekhanov Russian University of Economics
Graduate School of Service and Trade

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