Linear Encryption Against Eavesdropping on Remote State Estimation
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
This article considers the problem of eavesdropping on remote state estimation. Linear encryption strategies are used to protect the transmitted data. Two types of data transmission are considered: raw measurements generated by traditional sensors as well as innovations generated by smart sensors. For these two transmission scenarios, the encryption coefficients are obtained by formulating different optimization problems, with the target of maximizing the eavesdropper's estimation error covariance. For stable systems, the designed encryption strategies can effectively impair the eavesdropper's estimation performance. For unstable systems, the encryption can be designed to make the eavesdropper's estimation error covariance divergent. The encryption performance is evaluated through numerical simulations on both stable and unstable systems.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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