Mesh: A Supply Chain Solution with Locally Private Blockchain Transactions
Bibliographic record
Abstract
Abstract A major line of research on blockchains is geared towards enhancing the privacy of transactions through anonymity using generic non-interactive proofs. However, there is a good cluster of application scenarios where complete anonymity is not desirable and accountability is in fact required. In this work, we utilize non-interactive proofs of knowledge of elliptic curve discrete logarithms to present membership and verifiable encryption proof, which offers plausible anonymity when combined with the regular signing process of the blockchain transactions. The proof system requires no trusted setup, both its communication and computation complexities are linear in the number of set members, and its security relies on the discrete logarithm assumption. As a use-case for this scenario, we present Mesh which is a blockchain-based framework for supply chain management using RFIDs. Finally, the confidentiality of the transacted information is realized using a lightweight key chaining mechanism implemented on RFIDs. We formally define and prove the main security features of the protocol, and report on experiments for evaluating the performance of the modified transactions for this system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".