Adversarial wiretap channel with public discussion
Bibliographic record
Abstract
Wyner's elegant model of wiretap channel exploits noise in the communication channel to provide perfect secrecy against a computationally unlimited passive eavesdropper, without requiring a shared key. We consider an adversarial model of wiretap channel in which the adversary is active: it selects a fraction ρrof the transmitted codeword to eavesdrop, and a fraction ρwto corrupt by “adding” adversarial error. The model is interesting as it also captures networks adversaries in the setting of Secure Message Transmission [7]. It has been proved that secure transmission (in one message-round) is possible if and only if ρr+ ρwr+ρw> 1, as long as the union of the sets of read and corrupted components do not cover the whole codeword. This makes the results applicable to a much wider range of scenarios. We formalize the model of AWTPPD protocol, and derive tight bounds for the two communication efficiency measures, information rate and message-round complexity (upper bound, and lower bound respectively). We also construct a rate optimal protocol family with minimum number of message-round. We show an application of these results to the Secure Message Transmission with Public Discussion (SMT-PD). In particular we show a new lower bound on the transmission rate of these protocols, and present a new construction of an optimal SMT-PD protocol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".