Bibliographic record
Abstract
During pandemics, health officials usually recommend access monitoring and regulation protocols/systems in places that are major activity centers. As organizations adhere to those recommendations, they often fail to implement proper privacy requirements to prevent privacy loss of the users of those protocols or systems. This is a very timely issue as health authorities across the world are increasingly putting these regulations in place to mitigate the spread of the current pandemic. A number of solutions have been proposed to mitigate these privacy issues existing in current models of contact tracing or access regulations systems. However, a prevalent pattern among these solutions are they mainly focus on protecting users privacy from server side and involve Bluetooth based ephemeral identifier exchange between users. Another pattern is all the current solutions try to solve the problem in city-wide or nation wide level. In this paper, we propose a system, PAARS, which approaches the privacy issues in access monitoring/regulation systems from a micro level. We solve the privacy issues in access monitoring/regulation systems without any exchange of any ephemeral identifiers between users. Moreover, our proposed system provides privacy on both server side and the user side by using secure hashing and differential privacy mechanism.
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.
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 | 0.003 | 0.002 |
| Open science | 0.004 | 0.009 |
| 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".