NFT-Based Data Marketplace with Digital Watermarking
Bibliographic record
Abstract
In today's digital world, enterprises and individuals are generating massive data that is potentially useful for many data consumers with data driven applications. The emergence of data marketplaces is a step toward helping the data owners to monetize their digital assets and get connected to the potential buyers. The current data marketplaces cannot handle the challenges related to data ownership claims, illegal redistribution, and data ownership traceability. To overcome these problems in a general-purpose market, we propose a marketplace based on watermarking and Non-Fungible Token (NFT) technologies. In the proposed NFT-based marketplace, the owner's data is stored as an NFT where the underlying content of the NFT holds the watermarked data. The watermarked data is obtained by embedding some information about the owners and the buyers into the original data. The embedded information can later be extracted to identify the owner and the buyer of the traded data. Furthermore, the transactions corresponding to the NFT provide verifiable ownership proof and traceable ownership history. A Proof-Of-Concept (POC) implementation of the proposed marketplace that will be integrated within AI-Gallery Data Marketplace service in Huawei Cloud is presented for trading image data. An extensive set of experiments to measure the gas consumption on the blockchain and evaluate the robustness of the watermarked assets against 51 attacks are performed. Finally, a method based on error correction codes is proposed for improving the watermarking robustness in the implemented marketplace. The link for the codes and the POC demo is provided in the appendix.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".