Advances and Challenges in Cloud Data Storage Security: A Systematic Review
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 significantly changed how data is stored by offering enhanced flexibility and scalability.However, its rapid growth has introduced serious security challenges, particularly concerning data integrity, confidentiality, and availability.This systematic review investigates recent research in cloud data storage security, focusing on research published between 2020 and 2024.A structured selection process led to the inclusion of 77 relevant studies that addressed key research questions.The review synthesizes current knowledge, identifies ongoing challenges, and evaluates six main security techniques, including, encryption, access control, data loss prevention (DLP), blockchain, machine learning, and data redundancy.Each method is analyzed based on its principles, application context, advantages, and limitations, along with a comparative assessment.Encryption is widely adopted and offers strong confidentiality but may reduce system performance.Access control enables accurate access management but is often complex to implement.DLP helps prevent sensitive data leaks but can result false positives.Blockchain improves transparency and trust but introduces latency and integration challenges.Machine learning enhances anomaly detection but depends on large datasets and computational resources.Data redundancy supports data availability but increases storage costs.The findings show that relying on a single method is not sufficient to ensure a complete data protection in cloud environments.A multi-layered approach, integrating various techniques, is necessary, particularly with the increased reliance on cloud services due to the expansion of the Internet of Things and the impact of the COVID-19 pandemic.This review contributes to the field by offering a comprehensive comparison of modern security models and provides direction for future research.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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