Location Verification of Wireless Internet Clients: Evaluation and Improvements
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Bibliographic record
Abstract
Client Presence Verification (CPV) was proposed in previous literature as a delay-based location verification algorithm that iteratively estimates Internet delays to corroborate assertions about a client's geographic presence in a prescribed region, e.g., before granting access to a location-based service. We evaluate CPV's performance in the presence of clients that use 802.11 networks by analyzing the following factors: the number of such clients in the network, how far adversaries are from their true locations, and the number of CPV iterations required to neutralize the effect of wireless networks. We use a mix of real-world traffic measurements from PlanetLab and existing wireless-delay probability models to create the evaluation datasets. The results indicate that, while wireless delays affect CPV's performance (e.g., from 3 to ~4.7 percent false reject/accept rates), CPV can mitigate the impact of such delays by performing more delay measurements prior to location verification. This work highlights the importance of including mitigation capabilities while designing security-sensitive applications and protocols to deal with the effect of wireless delays. This will become increasingly important with the ubiquitous use of mobile devices that is expected to increase with the introduction of new computing and communication paradigms such as the Internet of Things.
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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 | 0.000 |
| 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) | 0.000 | 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