Enhancing Privacy and Usability in Blockchain Traceability Systems
Bibliographic record
Abstract
Blockchain technology has emerged as a promising solution for improving traceability across global supply chains, offering tamper-proof records and increased transparency.However, concerns related to data privacy, confidentiality, and interoperability continue to hinder widespread adoption.This paper proposes a comprehensive framework addressing these key challenges by combining privacy-preserving techniques-such as permissioned ledgers, zero-knowledge proofs, and verifiable credentials-with industry-driven data standards (GS1 EPCIS, W3C Verifiable Credentials).We first review the landscape of blockchain traceability solutions and outline critical requirements from regulatory and operational perspectives.Next, we detail our proposed privacy-preserving and interoperable architecture, incorporating off-chain storage, role-based permissions, and selective disclosure mechanisms to accommodate the diverse needs of modern supply chains.We illustrate these concepts through a high-level system design, accompanied by implementation considerations.Our evaluation highlights that successful adoption depends on carefully balancing transparency and confidentiality, supplemented by robust governance structures and standard APIs.The paper concludes by discussing future directions for blockchain traceability, emphasizing scalability, user-centric design, and cross-chain interoperability as critical enablers of a global, privacypreserving supply chain ecosystem.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".