Trifecta: Faster High-Throughput Three-Party Computation over WAN Using Multi-Fan-In Logic Gates
Bibliographic record
Abstract
Multi-party computation (MPC) has been a very active area of research, and recent industrial deployments exist. Practical MPC is currently limited to low-latency, high-throughput network setups, i.e., local-area networks (LAN). However, many use cases require the participation of different entities located in different data centers, i.e., communication over wide-area networks (WAN). Although, constant-round MPC exists, it has very high communication cost. In this paper we investigate the reduction of the round complexity of secret-shared based multi-party computation. We propose a new three-party computation protocol that allows to compute multi-fan-in gates in one round without any precomputation. Our protocol outperforms related work, including constant-round protocols, over WANs. For example, we improve throughput of AES-128 over WAN by a factor of more than 2.2x compared to related work.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".