Cryptographic Solution for Security Problem in Cloud Computing Storage During Global Pandemics
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 emerged as a potential substitute over traditional computing systems during the time of the COVID-19 pandemic. Almost all organizations shift their working from conventional ways to the online form of working. Most of the organizations are planning to permanently change some % of their work to online WFH (Work from Home) mode. There are numerous benefits of using cloud services in terms of cost, portability, platform independence, accessibility, elasticity, etc. But security is the biggest barrier when one wants to move towards cloud computing services, especially the cloud storage service. To overcome the problem of security in cloud storage systems, we have presented an approach for data security in cloud storage. The proposed approach uses the cryptographic methods and provides security and monitoring features to the user data stored in cloud storage systems. The proposed approach continuously monitors user’s data for any kind of modification by attackers. Thus, approach not only provides data security but also improves user’s trust on cloud based storage services.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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