Multipath Private Communication: An Information Theoretic Approach
Bibliographic record
Abstract
Sending private messages over communication environments under surveillance is an important challenge in communication security and has attracted attentions of cryptographers through time. We believe that resources other than cryptographic keys can be used for communication privacy. We consider private message transmission (PMT) in an abstract multipath communication model between two communicants, Alice and Bob, in the presence of an eavesdropper, Eve. Alice and Bob have pre-shared keys and Eve is computationally unbounded. There are a total of $n$ paths and the three parties can have simultaneous access to at most $t_a$, $t_b$, and $t_e$ paths. The parties can switch their paths after every $λ$ bits of communication. We study perfect (P)-PMT versus asymptotically-perfect (AP)-PMT protocols. The former has zero tolerance of transmission error and leakage, whereas the latter allows for positive error and leakage that tend to zero as the message length increases. We derive the necessary and sufficient conditions under which P-PMT and AP-PMT are possible. We also introduce explicit P-PMT and AP-PMT constructions. Our results show AP-PMT protocols attain much higher information rates than P-PMT ones. Interestingly, AP-PMT is possible even in poorest condition where $t_a=t_b=1$ and $t_e=n-1$. It remains however an open question whether the derived rates can be improved by more sophisticated AP-PMT protocols. We study applications of our results to private communication over the real-life scenarios of multiple-frequency links and multiple-route networks. We show practical examples of such scenarios that can be abstracted by the multipath setting: Our results prove the possibility of keyless information-theoretic private message transmission at rates $17\%$ and $20\%$ for the two example scenarios, respectively. We discuss open problems and future work at the end.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 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 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".