Bibliographic record
Abstract
We describe scalable algorithms for secure multiparty computation (SMPC). We assume a synchronous message passing communication model, but we do not assume the existence of a broadcast channel. Our main result holds for the case where there are n players, of which a 1/3-ε fraction are controlled by an adversary, for ε any positive constant. We describe an SMPC algorithm for this model that requires each player to send Õ(⁄n+mn + √n) messages and perform Õ(⁄n+mn + √n) computations to compute any function f, where m is the size of a circuit to compute f. We also consider a model where all players are rational. In this model, we describe a Nash equilibrium protocol that solves SMPC and requires each player to send Õ(⁄n+mn) messages and perform Õ(⁄n+mn) computations. These results significantly improve over past results for SMPC which require each player to send a number of bits and perform a number of computations that is Θ(n, m)
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.557 | 0.416 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".