Reflections on U-PriSM 2
Bibliographic record
Abstract
The Second Usable Privacy and Security for Mobile Devices Workshop (U-PriSM 2) was co-located with MobileHCI'13 in Munich, Germany. The U-PriSM 2 was an opportunity for researchers and practitioners to discuss research challenges and experiences around the usable privacy and security of mobile devices (smartphones and tablets). Security and privacy often involve having non-security experts, or even novice users, regularly making important decisions while their main focus is on other primary tasks. This is especially true for mobile devices where users can quickly and easily install apps, where user interfaces are minimal due to space constraints, and where users are often distracted by their environment. Likewise, mobile devices present unique privacy and security risks because they allow third-party applications access to personal information and sensor data. The amount and sensitivity of such personally identifying information is likely to increase as device functionality increases. The convergence of these factors means that improvements to security and privacy provisions on mobile devices are becoming increasingly important. Workshop participants had a chance to explore mobile device usage and the unique usable security and privacy challenges that arise, discuss proposed systems and ideas that address these needs, and work towards the development of design principles to inform future development in the area.
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 | 0.054 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.021 | 0.052 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".