Optimal message transmission protocols with flexible parameters
Bibliographic record
Abstract
In Secure message transmission (SMT) protocols two nodes in a network want to communicate securely, given that some of the nodes in the network are corrupted by an adversary with unlimited computational power. An SMT protocol uses multiple paths between the sender and a receiver to guarantee privacy and reliability of the message transmission. An (e, δ)-SMT protocol bounds the adversary's success probability of breaking privacy and reliability to e and δ, respectively. Rate optimal SMT protocols have the smallest transmission rate (amount of communication per one bit of message). Rate optimal protocols have been constructed for a restricted set of parameters.In this paper we use wire virtualization method to construct new optimal protocols for a wide range of parameters using previously known optimal protocols. In particular, we design, for the first time, an optimal 1-round (0, δ)-SMT protocol for n = (2 + c)t, c ≥ 1/t, where n is the number of paths between the sender and the receiver, up to t of which are controlled by the adversary. We also design an optimal 2-round (0, 0)-SMT protocol for n = (2 + c)t, c ≥ 1/t, with communication cost better than the known protocols. The wire virtualization method can be used to construct other protocols with provable properties from component protocols.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".