Bibliographic record
Abstract
Internet geolocation technology aims to determine the physical (geographic) location of Internet users and devices. It is currently proposed or in use for a wide variety of purposes, including targeted marketing, restricting digital content sales to authorized jurisdictions, and security applications such as reducing credit card fraud. This raises questions about the veracity of claims of accurate and reliable geolocation. We provide a survey of Internet geolocation technologies with an emphasis on adversarial contexts; that is, we consider how this technology performs against a knowledgeable adversary whose goal is to evade geolocation. We do so by examining first the limitations of existing techniques, and then, from this base, determining how best to evade existing geolocation techniques. We also consider two further geolocation techniques which may be of use even against adversarial targets: (1) the extraction of client IP addresses using functionality introduced in the 1.5 Java API, and (2) the collection of round-trip times using HTTP refreshes. These techniques illustrate that the seemingly straightforward technique of evading geolocation by relaying traffic through a proxy server (or network of proxy servers) is not as straightforward as many end-users might expect. We give a demonstration of this for users of the popular Tor anonymizing network.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.015 |
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".