UbiSecE: UbiLab’s Secure Cloud Environment for Public Health Research in Microsoft Azure
Bibliographic record
Abstract
The use of Personal Health Information (PHI) has become increasingly popular in public health research in recent years. However, many researchers have stored collected PHI in local databases or filesystems with limited centralized storage. This has raised concerns about cybersecurity, the lack of standards, and the absence of a data governance program. To address these issues, a cloud-based infrastructure was developed for public health research over PHI that meets the requirements of Ubilab, a public health research group at the University of Waterloo. UbiSecE, a Secure Cloud-Based Infrastructure for Public Health Research, was designed by adapting Microsoft Azure's cloud infrastructure to meet the needs of Ubilab. Relevant laws, regulations, and standards, such as PIPEDA, GPDR, FIPPA, and PHIPA, that govern the utilization of PHI for public health research were identified. Additionally, the lab's actors, social norms, processes, and collective problems were analyzed to establish the foundation of the data governance program in Azure. Azure's data governance architecture guidelines were followed to provide the primary governance mechanisms for evaluating, guiding, and monitoring UbiSecE resources and processes. To ensure the secure maintenance of PHI, role-based access controls were implemented for all users, and all governance processes were deployed via Azure. Furthermore, NIST 800-53 compliance was integrated for all deployed resources. UbiSecE offers a centralized, private, and secure environment for public health research, which enables different users with different roles to conduct research with PHI.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.020 |
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 source (direct Gemma or distilled Codex), 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".