Online Tracking of Kids and Teens by Means of Invisible Images
Bibliographic record
Abstract
The recent news of a large-scale online tracking campaign involving Facebook users, which gave way to systematic misuse of the collected user-related data, have left millions of people deeply concerned about the state of their online privacy as well as the state of the overall information security in the cyber world. While most to-date revelations pertaining to user tracking are related to websites and social media generally intended for adult online users, relatively little is known about the prevalence of online tracking in websites geared towards children and teens. In this paper, we first provide a brief overview of two laws that seek to protect the privacy of kids and teens online ? the US Children's Online Privacy Act (COPPA) and the EU General Data Protection Regulation (GDPR). Subsequently, we present the results of our study which has looked for potential signs of user tracking in twenty select children-oriented websites in case of a user located in the USA (where COPPA is applicable) as well as a user located in the EU (where GDPR is applicable). The key findings of this study are alarming as they point to overwhelming evidence of widespread and highly covert user tracking in a range of different children-oriented websites. The majority of the discovered tracking is in direct conflict with both COPPA and GDPR, since it is performed without parental consent and by third-party advertising and tracking companies. The results also imply that, relative to their US counterparts, the children residing in the EU may be somewhat less subjected (but are still significantly exposed) to tracking by third-party companies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".