Privacy-Preserving Ownership Transfer: Challenges and An Outlined Solution Based on Zero-Knowledge Proofs
Bibliographic record
Abstract
Although employing blockchain in supply chain management (SCM) can provide benefits in numerous aspects such as traceability, transparency, and more, using public blockchain for SCM may compromise the privacy of the supply chain participants and their business secrets. In this paper, we review recent papers that integrate blockchain with SCM and the papers that propose privacy-preserving approaches for public blockchain. Then, we identify the problem in the existing solutions. Additionally, we present an outline of a framework that enables entities in a supply chain to upload their data records anonymously. This framework preserves unlinkability when transferring product ownership. The proposed scheme allows data auditors, who can be the end customers of a supply chain, to access a product's history and verify the authenticity of the data while preserving the privacy of the data uploader. We demonstrate that supply chain data records follow a directed acyclic graph (DAG), similar to the data structure that maintains data records in version control systems (VCS). Hence, this insight could make the framework applicable for anonymous version control systems based on blockchain.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".