Dynamic inference control in privacy preference enforcement
Bibliographic record
Abstract
In pervasive (ubiquitous) environments, context-aware agents are used to obtain, understand, and share local contexts with each other so that the environments could be integrated seamlessly. Context sharing among agents should be made privacy-conscious. Privacy preferences are generally specified to regulate the exchange of the contexts, where who have rights under what conditions to have what contexts are designated. However, released contexts could be used to infer those unreleased. In particular, different contexts released could endanger the security of different contexts unreleased. The existing privacy preference specification platforms do not have a mechanism to prevent inference. To date, there have been very few inference control mechanisms specifically tailored to context management in pervasive (ubiquitous) environments. A Bayesian network based mechanism has been proposed to prevent privacy-sensitive contexts from being inferred from those to be released. Nevertheless, contexts in pervasive (ubiquitous) environments could change from time to time and are history dependent. In this paper, we propose to use dynamic Bayesian networks to track the most updated beliefs of the adversaries about the dynamic domains in order to evaluate which contexts in the domains could be released safely in various situations.
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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 | 0.000 | 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) | 0.001 | 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".