Provable multiple replication data possession with full dynamics for secure cloud storage
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
Summary Cloud storage has been gaining tremendous popularity among individuals and corporations because of its low maintenance cost and on‐demand services for the clients. To improve the availability and the reliability of critical data, storing multiple replicas on multiple servers is a commonly used strategy. Currently, several provable data possession (PDP) protocols for multiple replicas of dynamic data have been proposed to ensure the integrity of outsourced multi‐copy data, but the efficiency of these protocols on verifying multiple replicas one by one is not satisfactory. In this paper, we propose a provable multiple replication data possession protocol with full dynamics, named MR‐DPDP. In MR‐DPDP, we utilize a novel authenticated data structure called Merkle hash tree with rank to support both full dynamic data updates and efficient integrity verification. In addition, our construction with RSA signature can support both variable‐sized file blocks and public verification. Through security proof and performance evaluation, we demonstrate that MR‐DPDP not only is sound but also incurs less communication overhead when updating data blocks as well as verifying a proof of the integrity of multiple replicas. Copyright © 2015 John Wiley & Sons, Ltd.
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) | 0.000 | 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 | 0.004 |
| 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