A framework for context-aware authentication
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
Context-aware computing facilitates the human-computer interaction by sensing and processing information about users and their environments. The surrounding environment becomes a smart space that actively communicates with the system about its users. In this environment, authentication is an integral part of security of the whole system. In this work, we propose a framework to construct a context-aware authentication system, where users customize their preferences and set their rules for authenticating other members. The context-aware authentication service uses context-data to establish trust and to share secrets between parties without undermining each party's privacy. Users' preferences are intuitively declared via lexical descriptions and are then combined with fuzzy logics. The framework utilizes an approximate private matching protocol which is combined with Identity Based Encryption. Our model is based upon reliable cryptographic primitives that are combined effectively to achieve the design specification. This results in a very flexible, scalable authentication service that is both context-aware and privacy preserving. (8 pages)
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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 | 0.000 |
| 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