Key-based Reversible Data Masking for Business Intelligence Healthcare Analytics Platforms
Bibliographic record
Abstract
Business Intelligence (BI) is quickly becoming a very important tool for all aspects of data analytics. An area that lacks a strong implementation for BI is the healthcare field. BI healthcare analytics platforms facilitate the clinical analysis, financial analysis, supply chain analysis, as well as, fraud and HR analysis. The reason behind the lack of adoption in healthcare arises from the need to meet the legislated and perceived requirements of security and privacy when dealing with clinical information. A strong data masking module is developed based on the key-based reversible approach to protect patients' data privacy, while maintaining the data utility to meet the need for data analytics within BI platforms of the healthcare environment. To ensure the performance of the proposed module, a TPC-H Benchmark analysis is performed which verifies that the analytics results of the masked data are appropriate when compared to the existing masking and encryption methods. The developed module is shown to be secure against the common security threats such as linkage attacks and replay attacks. It uses minimal computational overhead when compared to its counterpart methods and meets the legal requirements to be used safely in the healthcare industry.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".