Ensuring Security and Privacy Preservation for Cloud Data Services
Bibliographic record
Abstract
With the rapid development of cloud computing, more and more enterprises/individuals are starting to outsource local data to the cloud servers. However, under open networks and not fully trusted cloud environments, they face enormous security and privacy risks (e.g., data leakage or disclosure, data corruption or loss, and user privacy breach) when outsourcing their data to a public cloud or using their outsourced data. Recently, several studies were conducted to address these risks, and a series of solutions were proposed to enable data and privacy protection in untrusted cloud environments. To fully understand the advances and discover the research trends of this area, this survey summarizes and analyzes the state-of-the-art protection technologies. We first present security threats and requirements of an outsourcing data service to a cloud, and follow that with a high-level overview of the corresponding security technologies. We then dwell on existing protection solutions to achieve secure, dependable, and privacy-assured cloud data services including data search, data computation, data sharing, data storage, and data access. Finally, we propose open challenges and potential research directions in each category of solutions.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".