A theoretical model for obfuscating web navigation trails
Bibliographic record
Abstract
Information about consumer's web navigation trails are increasingly being collected, analyzed, and used to target them with advertisements. This information can be quite personal, indicating individuals' likes and dislikes, as well as current and long term needs. While the ability to effectively target advertisements helps keep many sites and services on the web available freely, this practice has also raised privacy concerns. These concerns concern multiple factors: lack of transparency by the data aggregators and lack of control by the consumers. One viable approach that individuals can take to regain control is obfuscation, whereby real user requests are masked via the injection of noisy requests. In this paper, we describe a theoretical model and design for a web browser extension that relies on a trusted third party to generate fake HTTP requests (dummies). The dummy requests are generated as k different users' profiles surfing in parallel with the actual user. The value of k can be adjusted by the user to achieve the level of obfuscation they are comfortable with.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".