A Decentralized Approach to Certificate Authentication and Issuer Trust Using Blockchain
DOI:
https://doi.org/10.63665/fwxxsz73Keywords:
Safety This project centers on blockchain-based certificate authentication, emphasizing decentralization, tamper-proof digital credentials, and transparent issuer trust. It leverages smart contracts and cryptographic hashing to prevent forgery, ensure privacy, and enable peer-to-peer verification without reliance on centralized authorities. The focus is on building a resilient infrastructure that guarantees authenticity, minimizes verification time, and eradicates vulnerabilities in traditional certificate management systems.Abstract
Verifying the authenticity of educational degree certificates is critical, especially during recruitment, where forged documents can cause significant disruptions and productivity losses. Traditional verification methods rely heavily on manual processes and centralized databases, making them vulnerable to delays, errors, and data tampering. These systems lack a unified, secure platform for seamless interaction between issuers, holders, and verifiers. To address these limitations, this paper proposes a decentralized, blockchain-based certificate verification and issuer validation system. Utilizing Ethereum, the solution stores certificate hashes on the blockchain, ensuring data immutability and tamper resistance. Each participant—issuer, holder, validator, and verifier—is represented as a peer node in the network. A hash-based search mechanism significantly reduces certificate lookup time, even when the certificate is not found. Experimental evaluation shows the system is cost-effective in terms of gas consumption and offers fast, reliable verification. This integrated approach ensures secure, transparent, and efficient certificate management and validation.
Downloads
References
[1] A. Osseiran et al., “The foundation of the mobile and wireless communi cations system for 2020 and beyond: Challenges, enablers and technology solutions,” in Proc. IEEE Veh. Technol. Conf., 2013, pp. 1–5.
[2] Z.Xuetal.,“Age-awaredataselectionandaggregatorplacementfortimely federated continual learning in mobile edge computing,” IEEE Trans. Comput., vol. 73, no. 2, pp. 466–480, Feb. 2024.
[3] R. Bhardwaj et al., “Ekya: Continuous learning of video analytics models on edge compute servers,” in Proc. 19th USENIX Symp. Netw. Syst. Des. Implementation, 2022, pp. 119–135.
[4] X. Hou and S. Dey, “Motion prediction and pre-rendering at the edge to enable ultra-low latency mobile 6DoF experiences,” IEEE Open J. Commun. Soc., vol. 1, pp. 1674–1690, 2020.
[5] F. Nawab, D.Agrawal, and A. El Abbadi, “DPaxos: Managing data closer to users for low-latency and mobile applications,” in Proc. Int. Conf. Manage. Data, 2018, pp. 1221–1236.
[6] Amazon, “AWS wavelength for media & entertainment,” 2021. [On line].
[7] Z. Xu, Y. Fu, Q. Xia, and H. Li, “Enabling age-aware Big Data analytics in serverless edge clouds,” in Proc. IEEE Conf. Comput. Commun., 2023, pp. 1–10.
[8] E. Schurman and J. Brutlag, “The user and business impact of server delays, additional bytes, and HTTP chunking in web search,” Velocity Web Perform. Operations Conf., Oreilly, 2009.
[9] S.-C. Lin et al., “The architectural implications of autonomous driving: Constraints and acceleration,” in Proc. 23rd Int. Conf. Architectural Sup port Program. Lang. Operating Syst., 2018, pp. 751–766.
[10] S. Ma, S. Guo, K. Wang, W. Jia, and M. Guo, “A cyclic game for service- orientedresourceallocationinedgecomputing,”IEEETrans.Serv. Comput., vol. 13, no. 4, pp. 723–734, Jul./Aug. 2020.
[11] Z. Xu et al., “Collaborate or separate? distributed service caching in mobile edge clouds,” in Proc. IEEE Conf. Comput. Commun., 2020, pp. 2066–2075.
[12] Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobileedgecomputing:Thecommunicationperspective,”IEEECommun. Surveys Tuts., vol. 19, no. 4, pp. 2322–2358, Fourth Quarter 2017.
[13] X. Xia, F. Chen, Q. He, J. Grundy, M. Abdelrazek, and H. Jin, “Online collaborative data caching in edge computing,” IEEE Trans. Parallel Distrib. Syst., vol. 32, no. 2, pp. 281–294, Feb. 2021.
[14] J. Zhou, F. Chen, Q. He, X. Xia, R. Wang, and Y. Xiang, “Data caching optimization with fairness in mobile edge computing,” IEEE Trans. Serv. Comput., vol. 16, no. 3, pp. 1750–1762, May/Jun. 2023.
[15] R. Luo, H. Jin, Q. He, S. Wu, and X. Xia, “Enabling balanced data deduplication in mobile edge computing,” IEEE Trans. Parallel Distrib. Syst., vol. 34, no. 5, pp. 1420–1431, May 2023.
[16]X.Xiaetal.,“Formulatingcost-effectivedatadistribution strategies online foredgecachesystems,”IEEETrans.ParallelDistrib.Syst.,vol.33,no.12, pp. 4270–4281, Dec. 2022.
[17] E. Li, L. Zeng, Z. Zhou, and X. Chen, “Edge AI: On-demand accelerating deep neural network inference via edge computing,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 447–457, Jan. 2020.
[18] B.Lietal., “Cooperative assurance of cache data integrity for mobile edge computing,” IEEE Trans. Inf. Forensics Secur., vol. 16, pp. 4648–4662, 2021.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Authors
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
