Secure Enterprise Data Deduplication in the Cloud
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
With the advent of cloud computing as a new paradigm and technology, and the increased tendency of decision makers to envision a staged migration to cloud services, most enterprises are choosing to outsource their data to cloud storage providers, for better management of their IT resources, in terms of security, control, space and storage costs. In this context, assuming that the cloud service provider may not be trustworthy (i.e. is honest but curious), ensuring data privacy in all operations performed on enterprise data while these data reside in the Cloud is still a challenge. This paper proposes a novel twolevel data deduplication framework that can be used in cloud storage by enterprises. At the enterprise level, the enterprise performs cross-user data deduplication and outsources its data to the Cloud. At the cloud storage provider level, cross enterprise data deduplication is performed by the cloud service provider to further remove duplicates, resulting in cost and space savings. We argue that our framework will allow the enterprise to facilitate operations such as searching over encrypted data, sharing data within the enterprise, and downloading data from the Cloud directly, in a secure and efficient manner without the need to trust the cloud service provider.
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 | 0.000 | 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 | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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