Blockchain-enhanced Integrity Verification in Educational Content Assessment Platform: A Lightweight and Cost-Efficient Approach
<|reference_start|>Blockchain-enhanced Integrity Verification in Educational Content Assessment Platform: A Lightweight and Cost-Efficient Approach: The growing digitization of education presents significant challenges in maintaining the integrity and trustworthiness of educational content. Traditional systems often fail to ensure data authenticity and prevent unauthorized alterations, particularly in the evaluation of teachers' professional activities, where demand for transparent and secure assessment mechanisms is increasing. In this context, Blockchain technology offers a novel solution to address these issues. This paper introduces a Blockchain-enhanced framework for the Electronic Platform for Expertise of Content (EPEC), a platform used for reviewing and assessing educational materials. Our approach integrates the Polygon network, a Layer-2 solution for Ethereum, to securely store and retrieve encrypted reviews, ensuring both privacy and accountability. By leveraging Python, Flask, and Web3.py, we interact with a Solidity-based smart contract to securely link each review to a unique identifier (UID) that connects on-chain data with real-world databases. The system, containerized using Docker, facilitates easy deployment and integration through API endpoints. Our implementation demonstrates significant cost savings, with a 98\% reduction in gas fees compared to Ethereum, making it a scalable and cost-effective solution. This research contributes to the ongoing effort to implement Blockchain in educational content verification, offering a practical and secure framework that enhances trust and transparency in the digital education landscape.<|reference_end|>
@article{bayan2024blockchain-enhanced,
    title={Blockchain-enhanced Integrity Verification in Educational Content
  Assessment Platform: A Lightweight and Cost-Efficient Approach},
    author={Talgar Bayan, Richard Banach, Askar Nurbekov, Makhmud Mustafabek Galy,
  Adi Sabyrbayev, Zhanat Nurbekova},
    journal={arXiv preprint arXiv:2409.19828},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19828},
    primaryClass={cs.CR cs.SE}
}