Responsibility, Trust, and Monitoring Tools for End-User Account Security
Bibliographic record
Abstract
Threats to online accounts are increasingly more sophisticated and proactive defences may be insufficient.We explore the role of users in security monitoring of their activity logs.First, we designed and prototyped an account monitoring app that allows users to monitor the activity of many accounts at once.We present the prototype account monitoring app and the results of a lab study with 15 participants to explore its usability.Besides usability results, we identified external factors influencing adoption relating to user trust of security tools and service providers.We next conducted a second study of 170 participants in an online survey to explore responsibility, trust, and monitoring for account security.We identified a mismatch in perceived responsibility between users and service providers, explored the trust cues participants use to trust their service providers, and explored end user activity log practices for account monitoring.We also designed and evaluated two updated activity log designs based on feedback from our first study.and support.I am grateful for the opportunity you gave me to join your lab.Your commitment to the success of your students continues to inspire me.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".