Cloud Computing Environment: Research on Big Data Security and Privacy Protection Strategies
Bibliographic record
Abstract
Against the backdrop of rapid cloud computing development, big data has become an important resource across research, education, government and enterprise sectors, characterized by large scale, diverse types and high sensitivity. However, the openness and sharing features of cloud environments also bring severe challenges to data security and privacy protection. This paper first analyzes cloud computing architectures and the characteristics of big data, and describes the main security risks that arise throughout the data lifecycle (collection, transmission, storage and use), while summarizing common threat types and privacy leakage pathways. On this basis, it discusses key technical measures such as encryption, access control, differential privacy and federated learning, and proposes a protection strategy that integrates a multi-layered security defense with a compliance-oriented governance framework. Case studies are used to validate the feasibility and practical effectiveness of the proposed strategy in preventing data breaches and improving privacy protection. The results show that building a systematic, scalable security and privacy protection system not only effectively ensures the security and trustworthiness of big data in cloud environments, but also provides strong support for future intelligent and compliant data applications.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".