Towards models for quantifying the known adversary
Bibliographic record
Abstract
The known adversary threat model has drawn growing attention of the security community. The known adversary is any individual with elevated first-hand knowledge of a potential victim and/or elevated access to a potential victim's devices. However, little attention is given on how to carefully recruit paired participants for user studies, who are qualified as legitimate known adversaries. Also, there is no formal framework for detecting and quantifying the known adversary. We develop three models, inspired by Social Psychology literature, to quantify the known adversary in paired user studies, and test them using a case study. Our results indicate that our proposed adapted-relationship closeness inventory and known adversary inventory models could accurately quantify and predict the known adversary. We subsequently discuss how social network analysis and artificial intelligence can automatically quantify the known adversary using publicly available data. We further discuss how these technologies can help the development of privacy assistants, which can automatically mitigate the risk of sharing sensitive information with potential known adversaries.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".