Hardware-based DLAS: Achieving geo-location guarantees for cloud data using TPM and Provable Data Possession
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
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| 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