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Stream/Area:
-
Subject:
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Paper ID:
IJSREM67924
Volume:
10 |
10 |
Oct 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science Engineering (Data Science)
Guru Nanak Institute of Technology
Computer Science Engineering (Data Science)
Guru Nanak Institute of Technology
Computer Science Engineering (Data Science)
Guru Nanak Institute of Technology
Computer Science Engineering (Data Science)
Guru Nanak Institute of Technology
Computer Science Engineering (Data Science)
Guru Nanak Institute of Technology
The widespread use of data-hungry artificial intelligence systems has turned specialised empirical datasets into valuable economic assets. Yet today’s data exchanges depend on centralized intermediaries, which create single points of failure, obscure chains of custody and settle payments non-atomically. Evidence also suggests that as many as 62% of unvetted public records contain structural flaws, repeated observations or faulty labels, all of which weaken the models trained on them. Distributed ledgers offer tamper-proof audit trails and trustless escrow through smart contracts, but a plain blockchain only ensures that a transaction is carried out; it cannot judge whether the data behind it is meaningful or original. This review surveys recent work on hybrid marketplaces that pair blockchain settlement with machine-learning-based quality control. We describe the trading model at a conceptual level, propose an architectural taxonomy covering off-chain storage and smart contract state machines, and assess algorithmic components for statistical completeness, locality-sensitive hashing and dynamic valuation. We then review the key literature, compare the approaches in a feature matrix and discuss the main open problems: adversarial data poisoning, algorithmic front-running and compliance with privacy regulation.
KEYWORDS
Combining artificial intelligence with distributed ledger technology tackles a core problem of today’s digital economy: how to monetize data in a verifiable, trustless and fair way. Traditional centralized brokers bring single points of failure, non-transparent pricing and widespread exposure to counterfeit assets. Likewise, a blockchain without AI support can only guarantee that transactions are valid and stays blind to the quality and originality of the data it records. By joining decentralized IPFS storage and conditional smart contract escrow with automated machine learning quality profiling, Min-Hash locality-sensitive deduplication and algorithmic valuation, these architectures provide an end-to-end framework for trading digital assets reliably [1]–[15].
[1] V. Upadhyay, J. Kokila and A. Vaish, “A dual framework for optimized data storage and retrieval using lightweight python blockchain and scalable smart contracts with IPFS,” in Proc. IEEE Int. Conf. Adv. Comput. Commun. Syst. (ICACCS), 2026. [2] D. Abisha and S. K. Selvaraj, “AQUASTREAM: A decentralized data marketplace for secure sharing and access,” IEEE Access, vol. 13, pp. 11045–11058, 2025. [3] S. Kayikci and T. M. Khoshgoftaar, “Blockchain meets machine learning: A survey,” IEEE Trans. Big Data, vol. 10, no. 4, pp. 389–406, 2024. [4] IEEE Technical Committee on Data Engineering, “Decentralized data marketplace using blockchain and IPFS,” in Proc. IEEE Int. Conf. Blockchain Informatics (ICBI), pp. 240–251, 2023. [5] T. McConaghy et al., “Ocean Protocol: Tools for the Web3 data economy,” Technical Whitepaper, Ocean Protocol Foundation, 2020. [6] P. Sharma, R. K. Singh and A. K. Verma, “Decentralized industrial IoT data valuation via LightGBM,” IEEE Trans. Ind. Informatics, vol. 22, no. 1, pp. 412–423, 2026. [7] U. Bodkhe, S. Tanwar and R. Gupta, “Transformer-based smart contract auditing in data markets,” Expert Syst. with Appl., vol. 248, Art. no. 123450, 2026. [8] A. K. Das, S. Mohanty and N. Garg, “Medical telemetry monetization via Layer-2 rollups,” IEEE Internet of Things J., vol. 13, no. 2, pp. 1890–1902, 2026. [9] S. Tanwar, R. Kumar and P. K. Sharma, “Threat mitigation framework for Web3 data exchanges,” Comput. Netw., vol. 241, Art. no. 110214, 2026. [10] V. Anand, S. Gupta and M. K. Mishra, “Shapley-driven agritech data monetization platform,” IEEE Access, vol. 14, pp. 4512–4525, 2026.
Publication Date
10/13/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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