Improved OT Extension for Transferring Short Secrets.
Bibliographic record
Abstract
We propose an optimization and generalization of OT extension of Ishai et al. of Crypto 2003. For computational security parameter k, our OT extension for short secrets offers O(log k) factor performance improvement in communication and computation, compared to prior work. In concrete terms, for today’s security parameters, this means approx. factor 2-3 improvement. This results in corresponding improvements in applications relying on such OT. In particular, for two-party semi-honest SFE, this results in O(log k) factor improvement in communication over state of the art Yao Garbled Circuit, and has the same asymptotic complexity as the recent multi-round construction of Kolesnikov and Kumaresan of SCN 2012. For multi-party semi-honest SFE, where their construction is inapplicable, our construction implies O(log k) factor communication and computation improvement over best previous constructions. As with our OT extension, for today’s security parameters, this means approximately factor 2 improvement in semi-honest multi-party SFE. Our building block of independent interest is a novel IKNP-based framework for 1-out-of-n OT extension, which offers O(logn) factor performance improvement over previous work (for n ≤ k), and concrete factor improvement of up to 5 for today’s security parameters (n=k=128). Our protocol is the first practical OT with communication/computation cost sublinear in the security parameter (prior sublinear constructions Ishai et al. [15, 16] are not efficient in concrete terms).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".