Broadcast-enhanced Key Predistribution Schemes.
Bibliographic record
Abstract
Schemes. These schemes are suitable for networks with access to a trusted base station and an authenticated broadcast channel. We demonstrate that the access to these extra resources allows for the creation of broadcast-enchanced key predistribution schemes with advantages over key predistribution schemes such as flexibility and more efficient revocation. There are many possible ways to implement broadcast-enhanced key predistribution schemes, and we propose a framework for describing and analysing them. In their paper ‘From key predistribution to key redistribution’, Cichoń, Gol¸ebiewski and Kutylowski propose a scheme for ‘redistributing ’ keys to a wireless sensor network using a broadcast channel after an initial key predistribution. We classify this as a broadcast-enhanced key predistribution scheme and analyse it in that context. We provide simpler proofs of some results from their paper, give a precise analysis of the resilience of their scheme, and discuss possible modifications. In the latter half of the paper we study two scenarios where broadcast-enhanced key predistribution schemes may be particularly desirable and consider the design goals to prioritise in each case. For each scenario we propose a suitable family of broadcastenhanced key predistribution schemes and our analysis demonstrates their effectiveness in achieving their aims in resource-constrained networks. 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".