On Verifying Dynamic Multiple Data Copies over Cloud Servers.
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
Currently, many individuals and organizations outsource their data to remote cloud service providers (CSPs) seeking to reduce the maintenance cost and the burden of large local data storage. The CSP offers paid storage space on its infrastructure to store customers ’ data. Replicating data on multiple servers across multiple data centers achieves a higher level of scalability, availability, and durability. The more copies the CSP is asked to store, the more fees the customers are charged. Therefore, customers need to be strongly convinced that the CSP is storing all data copies that are agreed upon in the service contract, and the data-update requests issued by the customers have been correctly executed on all remotely stored copies. In this paper we propose two dynamic multi-copy provable data possession schemes that achieve two main goals: i) they prevent the CSP from cheating and using less storage by maintaining fewer copies, and ii) they support dynamic behavior of data copies over cloud servers via operations such as block modification, insertion, deletion, and append. We prove the security of the proposed schemes against colluding servers. Through theoretical analysis and experimental results, we demonstrate the performance of these schemes. Additionally, we discuss how to identify corrupted copies by slightly modifying the proposed schemes.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.009 | 0.035 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it