High-Rate Secret Key Generation Using Physical Layer Security and Physical Unclonable Functions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Physical layer security (PLS) can be adopted for efficient key generation and sharing in secured wireless systems. The inherent random nature of the wireless channel and the associated channel reciprocity (CR) are the main pillars for realizing PLS techniques. However, for applications that involve air-to-air (A2A) transmission, such as unmanned aerial vehicle (UAV) applications, the channel does not generally have sufficient randomness to enable reliable key generation. Therefore, this work proposes a novel system design to mitigate the channel randomness constraint and enable a high-rate secret key generation process. The proposed system integrates physically unclonable functions (PUFs) and CR to generate and exchange secret keys between two nodes securely. Moreover, an adaptive and controllable artificial fading (AF) level with interleaving is used to mitigate the impact of low randomness variations in the wireless channel. Moreover, we propose a novel bit extraction scheme to reduce the number of overhead bits required to share the intermediate keys. The obtained Monte Carlo simulation results show that the proposed system can operate efficiently even when the channel is nearly flat or time-invariant. Consequently, the time required for generating and sharing a key is significantly shorter than conventional techniques. Furthermore, the results show that a key agreement can be reached at the first trial for moderate and high signal-to-noise ratios (SNRs) substantially faster than other PLS techniques. Adopting the AF into static channels managed to reduce the mismatch ratio between the generated secret sequences and degrade the eavesdropper’s capability to predict the secret keys.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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