Enhancing Data Integrity in Blockchain Oracles Through Multi-Label Analysis
Source abstract
Abstract
The oracle problem in blockchain refers to the critical need to obtain reliable external data for the correct execution of smart contracts. Dependence on these external sources involves risks of manipulation and inaccuracies that can compromise automated decisions on the blockchain. Although solutions such as decentralized oracles and consensus mechanisms have been developed, ensuring data integrity remains a significant challenge. A validation approach based on Integrity Multi-level Weighted Voting (IMWV) is proposed to address this need. This model employs a multi-level weighted voting scheme, assigning differentiated weights to Oracle data sources and their derived decisions. It optimizes the accuracy of validated information and reduces variability in volatile environments, such as coffee futures contracts in Colombia. After conducting 60 tests, the system achieved 59 successful transactions, confirming the effectiveness of the validation process. A single failure highlighted the importance of continuous monitoring to identify and correct errors, thus protecting the system’s integrity. This IMWV-based proposal represents a significant contribution by increasing the reliability of smart contracts, offering an adaptable approach to address the oracle problem in blockchain, and laying the groundwork for future research.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Spam and Phishing Detection · Blockchain Technology Applications and Security · Imbalanced Data Classification Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Telematics Engineering Group (GIT), Telematics Engineering, University of Cauca, Popayán 190002, Colombia
Funding
Sistema General de Regalías (SGR) de Colombia
References (40)
Show source-provided references
1. Before Ethereum. The Origin and Evolution of Blockchain Oracles10.1109/ACCESS.2023.3279106 ↗
2. Blockchain Based Data Integrity Verification for Large-Scale IoT Data10.1109/ACCESS.2019.2952635 ↗
3. Trustworthy Blockchain Oracles: Review, Comparison, and Open Research Challenges10.1109/ACCESS.2020.2992698 ↗
4. Breidenbach, L., Cachin, C., Chan, B., Coventry, A., Ellis, S., Juels, A., Koushanfar, F., Miller, A., Magauran, B., and Moroz, D. (2021, January 01). Chainlink 2.0: Next Steps in the Evolution of Decentralized Oracle Networks. Withepaper 2021, pp. 1–136. Available online: https://research.chain.link/whitepaper-v2.pdf.
5. Toward Trustworthy DeFi Oracles: Past, Present, and Future10.1109/ACCESS.2022.3179374 ↗
6. Sata, B., Berlanga, A., Chanel, C.P.C., and Lacan, J. (2021, January 27–30). Connecting AI-based Oracles to Blockchains via an Auditable Auction Protocol. Proceedings of the 2021 3rd Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS), Paris, France.10.1109/BRAINS52497.2021.9569808 ↗
7. A preliminary study on artificial intelligence oracles and smart contracts: A legal approach to the interaction of two novel technological breakthroughs10.1016/j.clsr.2023.105869 ↗
8. Wang, Y., Liu, H., Wang, J., and Wang, S. (2020, January 11–13). Efficient data interaction of blockchain smart contract with oracle mechanism. Proceedings of the 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.10.1109/ITAIC49862.2020.9338784 ↗
9. INCHAIN: A cyber insurance architecture with smart contracts and self-sovereign identity on top of blockchain10.1007/s10207-023-00741-8 ↗
10. Data science as knowledge creation a framework for synergies between data analysts and domain professionals10.1016/j.techfore.2021.121160 ↗
11. Construction information authentication and integrity using blockchain-oriented watermarking techniques10.1016/j.autcon.2022.104570 ↗
12. Ordoñez, C.C., Organero, M.M., Ramirez-Gonzalez, G., and Corrales, J.C. (2024). Smart Contracts as a Tool to Support the Challenges of Buying and Selling Coffee Futures Contracts in Colombia. Agriculture, 14.10.3390/agriculture14060845 ↗
13. A review of methods for imbalanced multi-label classification10.1016/j.patcog.2021.107965 ↗
14. Comprehensive comparative study of multi-label classification methods10.1016/j.eswa.2022.117215 ↗
15. Oracle-Supported Dynamic Exploit Generation for Smart Contracts10.1109/TDSC.2020.3037332 ↗
16. OWL: A data sharing scheme with controllable anonymity and integrity for group users10.1016/j.comcom.2023.07.022 ↗
17. Begg, T.C.-C. (2015). Database Systems A Practical Approach to Design, Implementation, and Management, Always Learning Pearson. [6th ed.].
18. Blockchain Oracles: State-of-the-Art and Research Directions10.1109/ACCESS.2022.3184726 ↗
19. Heiss, J., Eberhardt, J., and Tai, S. (2019, January 14–17). From oracles to trustworthy data on-chaining systems. Proceedings of the 2019 2nd IEEE International Conference on Blockchain (Blockchain), Atlanta, GA, USA.10.1109/Blockchain.2019.00075 ↗
20. Hedging with commodity futures: Evidence from the coffee market in Vietnam
21. (2019, May 01). Min Agricultura, Agricultura por Contrato. Coseche y Venda a la Fija, Available online: https://www.minagricultura.gov.co/Paginas/Coseche-venda-a-la-fija.aspx.
22. The Financialization of Coffee, Cocoa and Cotton Value Chains: The Role of Physical Actors10.1111/dech.12802 ↗
23. Graph-Based Profiling of Blockchain Oracles10.1109/ACCESS.2023.3254535 ↗
24. Blockchain-Envisioned Trusted Random Oracles for IoT-Enabled Probabilistic Smart Contracts10.1109/JIOT.2021.3072293 ↗
25. Ellis, S., Juels, A., and Nazarov, S. (2017, May 01). ChainLink: A Decentralized Oracle Network. Available online: https://link.smartcontract.com/whitepaper.
26. Srinawakoon, S. (2018, May 01). Decentralized Data Curation Protocol. Bandprotocol. Available online: https://www.bandprotocol.com/.
27. Apis, D., and Benligiray, B. (2018, July 01). Decentralized APIs for Web 3.0. api3.org. Available online: https://api3.org/.
28. Oracle, T. (2023, July 01). The Oracle TELLOR. Tellor.io. Available online: https://tellor.io/whitepaper/.
29. (2023, July 01). NEST: Decentralized Martingale Network B the Accuracy of the NEST Price. nestprotocol.org. Available online: https://www.nestprotocol.org/doc/ennestwhitepaper.pdf.
30. Chainx (2019, July 01). Chainx Whitepaper. chainx.org. Available online: https://chainx.org/.
31. Gazzola, P., Pavione, E., Barge, A., and Fassio, F. (2023). Using the Transparency of Supply Chain Powered by Blockchain to Improve Sustainability Relationships with Stakeholders in the Food Sector: The Case Study of Lavazza. Sustainability, 15.10.3390/su15107884 ↗
32. Ordoñez, C.C., Gonzales, G.R., and Corrales, J.C. (2024). Blockchain and agricultural sustainability in South America: A systematic review. Front. Sustain. Food Syst., 8.10.3389/fsufs.2024.1347116 ↗
33. Sustainability strategies by companies in the global coffee sector10.1002/bse.2596 ↗
34. Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation10.1016/j.comnet.2021.108149 ↗
35. An empirical analysis of binary transformation strategies and base algorithms for multi-label learning10.1007/s10994-020-05879-3 ↗
36. Improving Neural Network-Based Multi-Label Classification With Pattern Loss Penalties10.1109/ACCESS.2024.3386841 ↗
37. A thorough experimental comparison of multi-label methods for classification performance10.1016/j.patcog.2024.110342 ↗
38. Li, S.Z., and Jain, A. (2009). Ensemble Learning BT—Encyclopedia of Biometrics, Springer.10.1007/978-0-387-73003-5 ↗
39. Analysis of variance (ANOVA)10.1016/0169-7439(89)80095-4 ↗
40. Peppes, N., Daskalakis, E., Alexakis, T., Adamopoulou, E., and Demestichas, K. (2021). Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0. Sensors, 21.10.3390/s21227475 ↗