Advances and Challenges in Cloud Data Storage Security: A Systematic Review
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.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".