Providing a data location assurance service for cloud storage environments
Bibliographic record
Abstract
In the cloud storage environment, the geographic location of the data has profound impacts on its privacy and security; it is due to the fact that the data stored on the cloud will be subject to the laws and regulations of the country where it is physically stored. This is one of the main reasons why companies that deal with sensitive data (e.g., health related data) cannot adopt cloud storage solutions. In order to ensure the rapid growth of cloud computing, we need a data location assurance solution which not only works for existing cloud storage environments but also influences those companies to adopt cloud storage solutions. In this paper, we present a Data Location Assurance Service (DLAS) solution for the well-known, honest-but-curious server model of the cloud storage environment; the proposed DLAS solution facilitates cloud users not only to give preferences regarding their data location but also to receive verifiable assurance about their data location from the Cloud Storage Provider (CSP). This paper also includes a detailed security and performance analysis of the proposed DLAS solution. Unlike other solutions, the DLAS solution allows a user to give a negative location preference regarding his/her data and works for CSPs (e.g., Windows Azure) that practice geo-replication of data (to ensure availability of data in case of natural disasters). Our proposed DLAS solution is based on cryptographic primitives such as zero knowledge sets protocol and ciphertext-policy attribute based encryption. According to the best of our knowledge, we are the first to propose a nongeolocation based solution of this kind.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".