Bibliographic record
Abstract
In the wiretap model of secure communication, Alice is connected to Bob over a noisy channel that is eavesdropped by Eve. The goal is to provide (asymptotic) reliability and perfect secrecy assuming that the Eve has unlimited computational power. The model has attracted considerable attention in recent years because it provides a natural model for passive eavesdropping in wireless communication. We consider a wiretap model with active adversaries, and define adversarial wiretap (AWTP) channels using a (ρr, ρw) wiretap adversary who can read a fraction ρr, and modify a fraction ρwof a sent codeword. The code components that are read and/or modified can be chosen adaptively, and the subsets of read and modified components could be different. AWTP codes provide secrecy and reliability for communication over AWTP channels. We define the security and reliability of AWTP channels and use these definitions to evaluate the security and reliability of codes for these channels. This paper has two main contributions. First, we prove an upper bound on the rate of AWTP codes for (ρr, ρw)-AWTP channels. Second, we give an explicit construction of a perfectly secure AWTP code family with efficient decoding that achieves the bound and, hence, obtain the secrecy capacity of AWTP channels for large alphabets. AWTP model is a natural extension of Wyner's wiretap models, and somewhat surprisingly, it is also closely related to a seemingly unrelated cryptographic primitive, secure message transmission (SMT). This relation results in a new (and the only known) bound on the transmission rate of 1-round (ε, δ)-SMT protocols. We discuss our results, give their relations to other works, and propose directions for future 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".