Beyond privacy policies - assessing inherent privacy risks of consumer health services
Bibliographic record
Abstract
There is a rapidly growing market for direct-to-consumer health services offered through the Internet and other information and communication technologies (ICT). Personal health information is one of the most sensitive types of data;while consumer health services have many potential health benefits, privacy advocates have warned consumers about the privacy risks associated with the indiscriminate use of direct-to-consumer services. Some tools and methods have been developed to aid consumers in assessing the privacy risk of ICT-based consumer health services. Most of these methods focus on the privacy policies published by the service provider, and are limited to a particular class of service offerings, e.g., Personal Health Records. While these methods have proven useful in gauging the apparent risk associated with certain types of services, they fall short of addressing the inherent risks of an entire spectrum of different service types. Moreover, privacy policy based risk assessment falls short of catching some of the more subtle privacy threats, such as indirect information disclosure due to targeted advertisements and social computing. This paper attempts to fill this gap by proposing a complementary tool to aid consumers in gauging the inherent privacy risks associated with consumer health services. The tool was developed based on a systematic review of the types of services and their associated privacy risks.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".