Security Challenges in Healthcare Cloud Computing: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Healthcare data are very sensitive records that should not be made available to unauthorized people in order for protecting patient's information security. However, in progressed technologies as cloud computing which are vulnerable to cyber gaps that pose an adverse impact on the security and privacy of patients’ electronic health records and in these situations, security challenges of the wireless networks need to be carefully understood and considered. Recently, security concerns in cloud computing environment are a matter of challenge with rising importance. OBJECTIVE: In this study a systematic review to investigate the security challenges in cloud computing was carried out. We focused mainly on healthcare cloud computing security with an organized review of 210 full text articles published between 2000 and 2015. METHOD: A systematic literature review was conducted including PubMed, Science direct, Embase, ProQuest, Web of science, Cochrane, Emerald, and Scopus databases. FINDINGS: Using the strategies described, 666 references retrieved (for research question one 365, research question two 201, and research question three 100 references). IMPROVEMENTS: Review of articles showed that for ensuring healthcare data security, it is important to provide authentication, authorization and access control within cloud's virtualized network. Issues such as identity management and access control, Internet-based access, authentication and authorization and cybercriminals are major concerns in healthcare cloud computing. To manage these issues many involved events such as Hybrid Execution Model, VCC-SSF, sHype Hypervisor Security Architecture, Identity Management, and Resource Isolation approaches have to be defined for using cloud computing threat management processes.
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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".