Updatable Linear Map Commitments and Their Applications in Elementary Databases
Bibliographic record
Abstract
Linear map commitments allow the prover to commit to a vector, with the ability to prove the image of a linear map acting on the vector. In this paper, we propose linear map commitments with updatable feature and perfectly hiding property. Updatable feature means that the prover can update the commitment more efficiently than recompute the commitment when some of the entries in the committed vector are changed. Perfectly hiding property ensures the commitment reveals no information about the committed vector before opening. Then we present the implementation of our updatable linear map commitment (ULMC) over the 256-bit BN curve recommended in the SM9 standard, which provides around 100-bit security. The implementation shows that our ULMC schemes are efficient enough to support the elementary database constructions that simultaneously permit batching membership test, linear combination test, updatable feature and authenticity. Finally, we show that the ULMC-powered elementary databases are capable of supporting various applications where privacy and trust are the first priority such as exam result management systems, Internet of Things (IoT) management systems and business operations between banks and enterprises.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| 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".