Proof of retrieval and ownership protocols for enterprise-level data deduplication
Bibliographic record
Abstract
The cloud computing paradigm is emerging as the next big thing in the world of information technology. Cloud technology offers a completely new set of benefits and savings in terms of computational costs, storage costs, bandwidth and transmission costs to its users. Cloud storage represents one of the most popular cloud services used. Data deduplication is a promising practice which facilitates saving high volumes of storage by allowing the cloud provider to store only a single copy of duplicated data. Client-side data deduplication offers additional savings in terms of bandwidth and storage. Applying data deduplication across enterprises also allows the cloud storage providers to apply data deduplication across users from different domains, providing additional savings. However, some of the advantages of cloud storage may be lost if additional steps are not taken to address some of the security and privacy issues associated with remotely stored data. Since users outsource their data to the cloud, they have to ensure the integrity of their data and its privacy from the cloud storage provider who now has complete access to it. In this paper, we present a solution for assuring data integrity in terms of proof of retrievability and ownership in the context of cross-user client-side data deduplication for medium- and small-sized enterprises. We propose a secure scheme which enables cloud service users to run their proof of retrievability with minimum storage and computational overheads in the case of honest-but-curious cloud storage providers. At the same time, the cloud storage provider will also be able to save digital storage by practising cross-enterprise data deduplication. We extend our scheme to include a proof of ownership scheme to assist the cloud in authenticating the user as the owner of the data before releasing it. Our scheme does not introduce any additional structural or storage overheads to either of the parties.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".