Cloud-hosted key sharing towards secure and scalable mobile applications in clouds
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
User data may be stored in a cloud to take advantage of its scalability, accessibility, and economics. However, data of a sensitive nature must be protected from being read in the clear by an untrusted cloud provider. It is also beneficial to provide finite time limits on access to the data by users. A key management scheme is proposed where encrypted key shares are stored in the cloud and automatically deleted based on passage of time or user activity. The accessibility of the data gradually expires and revocation occurs as a result of the loss of sufficient key shares. The process does not require additional coordination by the data owner, which is of advantage to a very large population of resource-constrained mobile users. The rate of expiration may be controlled through the initial allocation of shares and the heuristics for removal. Subscription to user data is maintained through regular re-generation of shares. A simulation of the scheme and also its implementation on commercial mobile and cloud platforms demonstrate its practical performance.
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 | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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