CPV: Delay-Based Location Verification for the Internet
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
The number of location-aware services over the Internet continues growing. Some of these require the client's geographic location for security-sensitive applications. Examples include location-aware authentication, location-aware access policies, fraud prevention, complying with media licensing, and regulating online gambling/voting. An adversary can evade existing geolocation techniques, e.g., by faking GPS coordinates or employing a non-local IP address through proxy and virtual private networks. We devise Client Presence Verification (CPV), a delay-based verification technique designed to verify an assertion about a device's presence inside a prescribed geographic region. CPV does not identify devices by their IP addresses. Rather, the device's location is corroborated in a novel way by leveraging geometric properties of triangles, which prevents an adversary from manipulating measured delays. To achieve high accuracy, CPV mitigates Internet path asymmetry using a novel method to deduce one-way application-layer delays to/from the client's participating device, and mines these delays for evidence supporting/refuting the asserted location. We evaluate CPV through detailed experiments on PlanetLab, exploring various factors that affect its efficacy, including the granularity of the verified location, and the verification time. Results highlight the potential of CPV for practical adoption.
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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