Server Location Verification (SLV) and Server Location Pinning
Bibliographic record
Abstract
We introduce the first known mechanism providing realtime server location verification. Its uses include enhancing server authentication by enabling browsers to automatically interpret server location information. We describe the design of this new measurement-based technique, Server Location Verification (SLV), and evaluate it using PlanetLab. We explain how SLV is compatible with the increasing trends of geographically distributed content dissemination over the Internet, without causing any new interoperability conflicts. Additionally, we introduce the notion of (verifiable)server location pinning(conceptually similar to certificate pinning) to support SLV, and evaluate their combined impact using a server-authentication evaluation framework. The results affirm the addition of new security benefits to the existing TLS-based authentication mechanisms. We implement SLV through a location verification service, the simplest version of which requires no server-side changes. We also implement a simple browser extension that interacts seamlessly with the verification infrastructure to obtain realtime server location-verification results.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".