BlurSense: Dynamic fine-grained access control for smartphone privacy
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
For many people, smartphones serve as a technical interface to the modern world. These smart devices have embedded on-board sensors, such as accelerometers, gyroscopes, GPS sensors, and cameras, which can be used to develop new mobile applications. However, the sensors also pose privacy risks to users. This work describes BlurSense, a tool that provides secure and customizable access to all of the sensors on smartphones, tablets, and similar end user devices. The current access control to the smartphone resources, such as sensor data, is static and coarse-grained. BlurSense is a dynamic, fine-grained, flexible access control mechanism, acting as a line of defense that allows users to define and add privacy filters. As a result, the user can expose filtered sensor data to untrusted apps, and researchers can collect data in a way that safeguards users' privacy.
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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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