Blockchain-Based Fair and Fine-Grained Data Trading With Privacy Preservation
Bibliographic record
Abstract
In this article, we propose a blockchain-based fair and privacy-preserving data trading scheme that supports fine-grained data selling. First, to achieve fairness for trading participants, by incorporating attribute-based credentials, encryption, and zero-knowledge proof, we design a data trading scheme where a buyer first publishes the required data attributes on the blockchain, and a data seller can demonstrate data availability in ciphertext by only disclosing the required attributes to a data buyer and proving the authenticity of data. A data buyer transfers funds only if the correct key material is uploaded to the blockchain. Second, to guarantee fine-grained data trading and preserve identity privacy, we build a Merkle hash tree on the ciphertexts of data with a signature on its root node, which allows a data seller to split data into blocks and remove the sensitive information from the data without affecting data availability verification. The public key of the data seller is not leaked to the data buyer during the trading. Moreover, different trading transactions from the same data seller cannot be linked. We formally prove that our scheme achieves the desired security properties: fairness and privacy preservation. Simulation results demonstrate the feasibility and efficiency of the proposed scheme.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".