A Security and Privacy Scoring System for Contact Tracing Apps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Contact tracing applications have flooded the marketplace, as governments worldwide have been working to release apps for their citizens. These apps use a variety of protocols to perform contact tracing, resulting in widely differing security and privacy assurances. Governments and users have been left without a standard metric to weigh these protocols and compare their assurances to know which are more private and secure. Although there are many ways to approach a quantitative metric for privacy and security, one natural way is to draw on the methodology used by the well-known common vulnerability scoring system (CVSS). For privacy, we applied consensus principles for contract tracing as a basis for comparing their relative privacy practices. For security, we performed attack modeling to develop a rubric to compare the security of respective apps. Our analysis shows that centralized Bluetooth with added location functionality has low privacy and security, while non-streaming GPS scored high in security and medium in privacy. Based on our methodology, only two apps were given a high ranking of privacy: Canada’s Covid Alert and Germany’s Corona Warn-App. They both used the Google/Apple Notification Framework as the basis for their design. To achieve comparable privacy, we recommend that future projects follow their examples in the following ways: minimizing the amount of data they collect and holding it for the shortest possible length of time; only having features necessary for the app’s main function; and releasing design details so that users can make informed decisions.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it