Data Security and Privacy Assurance Considerations in Cloud Computing for Health Insurance Providers
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
Cloud computing has been massively adopted in healthcare, where it attracts economic, operational, and functional advantages beneficial to insurance providers. However, according to Identity Theft Resource Centre, over twenty-five percent of data breaches in the US targeted healthcare. The HIPAA Journal reported an increase in healthcare data breaches in the US in 2016, exposing over 16 million health records. The growing incidents of cyberattacks in healthcare are compelling insurance providers to implement mitigating controls. Addressing data security and privacy issues before cloud adoption protects from monetary and reputation losses. This article provides an assessment tool for health insurance providers when adopting cloud vendor solutions. The final deliverable is a proposed framework derived from prominent cloud computing and governance sources, such as the Cloud Security Alliance, Cloud Control Matrix (CSA, CCM) v 3.0.1 and COBIT 5 Cloud Assurance.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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