Capturing P3P semantics using an enforceable lattice-based structure
Bibliographic record
Abstract
With the increasing amount of data collected by service providers, privacy concerns increase for data owners who must provide private data to receive services. Legislative acts require service providers to protect the privacy of customers. Privacy policy frameworks, such as P3P, assist the service providers by describing their privacy policies to customers (e.g. publishing privacy policy on websites). Unfortunately, providing the policies alone does not guarantee that they are actually enforced. Furthermore, a privacy-preserving model should consider the privacy preferences of both the data provider and collector. This paper discusses the challenges in development of capturing privacy predicates in a lattice structures. A use case study is presented to show the applicability of the lattice approach to a specific domain. We also present a comprehensive study on applying a lattice-based approach to P3P. We show capturing privacy elements of P3P in a lattice format facilitates managing and enforcing policies presented in P3P and accommodates the customization of privacy practices and preferences of data and service providers. We also propose that the outcome of this approach can be used on lattice-based privacy aware access control models [8].
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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 | 0.001 |
| 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 | 0.019 | 0.024 |
| 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; both teacher heads agree on what is shown here.
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".