Random sampling key revocation scheme for distributed sensor networks
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
Distributed key or node revocation facilitates the removal of compromised keys or nodes from a network without requiring a central authority. We propose a new revocation scheme, the random neighbourhood sampling node revocation, for distributed sensor networks. Our protocol is based on simple random sampling, a statistical method to estimate the property of the population through randomly sampling a minimal subset of the population. We exploit one of the inherent features of sensor networks: the sensor nodes are densely deployed, and there is a large overlap of the (wireless) coverage areas of any two neighbouring nodes. The revocation decision is made collectively by the neighbours of a suspicious node. However, instead of collecting the opinions of all neighbours of a suspicious node our scheme samples random subsets of the set of all its neighbours and of the node, which issued the warning. Our protocol is fully decentralized, incurs low communication cost, enables fast reaction to a detected intrusion, is false-detection tolerant and can be implemented with any pairwise key distribution scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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