PUPy: A Generalized, Optimistic Context Detection Framework for Implicit Authentication
Bibliographic record
Abstract
Devices like smartphones and laptops employ some form of user authentication to ensure that access to confidential data by the wrong user is avoided. Implicit authentication aims to limit the number of explicit authentications that a user is subjected to by using passive approaches to authenticate the user. Context detection frameworks aim to reduce explicit authentications by disabling explicit authentication entirely when appropriate. Since explicit and implicit authentication are not mutually exclusive, we can also use context detection frameworks to decide whether explicit or implicit authentication should be used when authentication is required. We present a novel context detection framework, PUPy, that uses sensed context data to infer and make available three values–privacy, unfamiliarity, and proximity–allowing clients of our framework, like authentication services, to better adapt to different contexts. As opposed to existing work, our context detection framework is based on an optimistic approach to context detection. Our assumption is that the absence of data, like the inability to detect nearby people or devices, can be taken as a sign that a context is safe. Such an optimistic approach may provide less security than a pessimistic approach, but provides a significantly improved user experience due to reducing the number of explicit authentications. We provide an Android implementation of the framework, including an API that allows other developers to contribute modules to the system. We also conduct a statistical analysis of our framework based on a large real-world dataset. We find that PUPy compares favourably to existing works, permitting a 77.2% reduction in the number of explicit authentications.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".