Balancing Privacy and Accountability for Industrial Mortgage Management
Bibliographic record
Abstract
Industrial mortgage enables companies to acquire loan for business venture or investment purposes by pledging their industrial assets to financial institutions. To prevent double-mortgage fraud of borrowers, information exchange among different financial institutions is necessary. On the other hand, it results in the privacy leakage of borrowers. In this article, we construct a blockchain-based accountable and privacy-preserving industrial mortgage scheme (BAPIM). BAPIM enables financial institutions to share the mortgage data of borrowers in an efficient and secure manner, that achieves the borrower identity privacy and accountability at the same time. Specifically, borrower identity is concealed on the blockchain by anonymous identity credential, while financial institutions can still uncover the identity of a misbehaving borrower if he pledges the same asset for multiple mortgages. We demonstrate that BAPIM achieves the desirable security properties and has high computational efficiency, so as to be suitable for the industrial mortgage management.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".