Three Improved Algorithms for Multi-path Key Establishment in Sensor Networks Using Protocols for Secure Message Transmission.
Bibliographic record
Abstract
In this paper, we propose a security model to capture active attacks against multi-path key establishment (MPKE) in sensor networks. Our model enhances previous models to capture more attacks and achieve essential security goals for multi-path key establishment. In this model, we can apply protocols for perfectly secure message transmission to solve the key establishment problem. We propose a new protocol for optimal one-round perfectly secure message transmission based on Reed-Solomon codes. Then we use this protocol to obtain two new multipath key establishment schemes that can be applied provided that fewer than one third of the paths contain an adversary node. Finally, we describe another MPKE scheme that tolerates a higher fraction (less than 1/2) of paths controlled by the adversary. This scheme is based on a new protocol for a weakened version of message transmission, which is very simple and efficient. Our multi-path key establishment schemes achieve improved security and lower communication complexity, as compared to previous schemes. 1
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".