Preserving Privacy Integration and Mining for Big Temporal Co-occurrence Patterns
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
Privacy policy, terms of use, public consent, reusable data, and transparency are trending words associated with the world wide web data such that privacy is now the responsibility of all stakeholders. Although privacy is a concern, integrating publicly available data may be for social good. For instance, integrating emergency calls, substance use, and overdose antagonist drug may help inform policies relating to emergency resources allocation, substance overdose antagonist drug distribution, and spiral effect of reducing overdose death. Hence, in this paper, we examine privacy preserving integration of public open data in the hierarchy of time and space. Our experimental result on four open datasets demonstrate the effectiveness of temporal and location hierarchy model in preserving privacy integration of big temporal co-occurrence data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.075 | 0.153 |
| Research integrity | 0.000 | 0.001 |
| 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