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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".