Time Correlation Analysis of Secret Key Generation via UWB Channels
Why this work is in the frame
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Bibliographic record
Abstract
Wireless channel characterization is a known methodology for the generation of secret keys, where the secrecy of the key depends on the spatial-temporal correlation properties of the channel which themselves arise due to the channel's physical constraints. Spatial correlations can be suitably addressed simply by moving from narrow band channels to ultra-wide band (UWB) channels, but this does not address temporal correlations, (i.e., the likelihood that two successive key generation processes will generate the same, or nearly the same, key). This work shows that the worst-case of the temporal correlation problem, (i.e., when an eavesdropper has perfect knowledge of all past channel characterizations) is effectively addressed by applying channel prediction to the available ensemble of channel characterisation events and removing the predictable ensembles. It is shown that this proposed prediction approach increases the security of the key generation process while simultaneously reduce the available amount of information for the key generation. Moreover, real-world experimental data is also applied to confirm that a linear prediction suffices in this case.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it