Hardware-based DLAS: Achieving geo-location guarantees for cloud data using TPM and Provable Data Possession
Bibliographic record
Abstract
Recently the lack of geo-location assurance of data in cloud storage has been identified as one of the main reasons why organizations that deal with sensitive data (e.g., financial data, health related data) cannot adopt a cloud storage solution even if they want to. In this paper, we present a Hardware-based Data geo-Location Assurance Solution (HDLAS), which is suitable for almost all cloud storage applications available today. Trusted Platform Module (TPM) and a cryptographic scheme called Provable Data Possession (PDP) are the basis of our solution. We define a new attack model for HDLAS which seems to be a realistic attack model for the existing cloud storage applications. With the combination of a GPS receiver and TPM, HDLAS is able to offer its clients not only the accurate geo-location of their data but also a hardware-based root of trust for that. Unlike many existing solutions, HDLAS works even if a piece of data is replicated into different storage servers. Furthermore we also illustrate how easily HDLAS can be adopted in existing Cloud Storage Providers such as Microsoft Azure.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".