Every step you fake: a comparative analysis of fitness tracker privacy and security
Bibliographic record
Abstract
Introduction Canadians, and many people around the world, are increasingly purchasing, and using, electronic devices meant to capture and record the relative levels of a person’s fitness. Unlike past fitness devices, such as pedometers, electronic fitness trackers are designed to display aggregate fitness information automatically on mobile devices and, frequently, on websites developed and controlled by the company that makes the given device. This automatic collection and dissemination of fitness data began with simply monitoring the steps a person had taken in a day. Contemporary consumer fitness wearables collect a broad range of data. The number of floors, or altitudinal changes, a person climbs a day is measured, levels and deepness of sleep, and heart rate activity are all captured by best-of-class consumer-level fitness trackers. And all of this data is of interest to the wearers of the devices, to companies interested in mining and selling collected fitness data, to insurance companies, to authorities and courts of law, and even potentially to criminals motivated to steal or access data retained by fitness companies. This report explores what information is collected by the companies which develop and sell some of the most popular wearables in North America. Moreover, it explores whether there are differences between the information that is collected by the devices and what companies say they collect, and what they subsequently provide to consumers when compelled to disclose all the personal information that companies hold about residents of Canada. In short, the project asks: Were data which are technically collected noted in companies’ privacy policies and terms of service and, if so, what protections or assurances do individuals have concerning the privacy or security of that data? What of that data is classified by the company as ‘personal’ data, which is tested by issuing legally compelling requests for the company to disclose all the personal data held on a requesting individual? Does the information received by the individual match what a company asserts is ‘personally identifiable information’ in their terms of service or privacy policies?
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 | 0.017 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".