Enhancing Mobile Content Privacy with Proxemics Aware Notifications and Protection
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
Given the widespread adoption of mobile devices and the private personal and work information they carry, casual or deliberate shoulder surfing is an increasing concern with these devices. We iteratively designed a tablet interface that detects when people nearby are looking at the screen, providing awareness through glyph notifications, and response through visual protections, and evaluated its use in two experiments. The results indicate that mobile content privacy management systems such as ours could help alleviate the cognitive and social burden of managing mobile device privacy in dynamic settings. We identify physical privacy behaviours and preferences that can inform the design of privacy notification and management protocols on mobile devices. We argue that such systems require subtlety so as not to advertise the users' intention for privacy, flexibility in addressing dynamic privacy needs and trustworthiness to promote adoption.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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