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

Zaffar Ahmed Shaikh,Laura Baitenova,Abdullah Ayub Khan,Natalia Ivolgina,Gulmira Zambinova,Natalia Yegina,Asif Ali Laghari,Sergey Evgenievich Barykin

doi:10.3390/app12052534

Abstract

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’ transactions. 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 simulations, the blockchain-enabled analytical model provides robust performance in terms of credential management and accreditation credibility problems.

Highlights

Certificate issuance and pre-verification by Higher Education Department (HED) recognized universities play a vital role in developing a platform and opportunities to pre-verify issued certificates through the e-portal mechanism
This paper proposes a novel collaborative model of machine learning and blockchain hyperledger sawtooth-enabled secure accreditation analysis procedure for investigating if a certificate credential is tamper-proof and verifying process credibility for the Higher Education Department
We proposed a blockchain hyperledger sawtooth-enabled secure distributed candidate accreditation and certificate credential verification process

Summary

Introduction

Certificate issuance and pre-verification by Higher Education Department (HED) recognized universities play a vital role in developing a platform and opportunities to pre-verify issued certificates through the e-portal mechanism. This kind of issue is widespread mostly in countries that are in the developing phases. The existing system is running for the certificate issuance process and accreditation, which is one of the main concerns towards pervasive and systemic concerns such as tampering and forgeries [6,7] It leads to the emerging problems of certificate duplication fraud, including document forgery, misrepresentation of credentials, a slight alteration in the cumulative percentage, and so on. The HED-recognized universities that provide academic completion certificates need to pre-verify and attest first, record all the ledgers in immutable storage that will never compromise the services of accreditation of the HED [8]

Methods

Results

Conclusion