The Effect of Platform Policies on App Privacy Compliance: A Study of Child-Directed Apps
Bibliographic record
Abstract
Over the past few years, the two dominant app platforms made major improvements to their policies surrounding child-directed apps. While prior work repeatedly demonstrated that privacy issues were prevalent in child-directed apps, it is unclear whether platform policies can lead child-directed apps to comply with privacy requirements, when laws alone have not. To understand the effect of recent changes in platform policies (e.g., whether they result in greater levels of compliance with applicable privacy laws), we conducted a large-scale measurement study of the privacy behaviors of 7,377 child-directed Android apps, as well as a follow-up survey with some of their developers. We observed a drastic decrease in the number of apps that transmitted personal data without verifiable parental consent and an increase in the number of apps that encrypted their transmissions using TLS. However, improper use of third-party SDKs still led to privacy issues (e.g., inaccurate disclosures in apps’ privacy labels). Our analysis of apps’ privacy practices over a period of a few months in 2023 and a comparison of our results with those observed a few years ago demonstrate gradual improvements in apps’ privacy practices over time. We discuss how app platforms can further improve their policies and emphasize the role of enforcement in making such policies effective.
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How this classification was reachedexpand
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".