CloudSky: A Controllable Data Self-Destruction System for Untrusted Cloud Storage Networks
Bibliographic record
Abstract
In cloud services, users may frequently be required to reveal their personal private information which could be stored in the cloud to used by different parts for different purposes. However, in a cloud-wide storage network, the servers are easily under strong attacks and also commonly experience software/hardware faults. As such, the private information could be under great risk in such an untrusted environment. Given that the presented personal sensitive information is usually out of user's controlin most cloud-based services, ensuring data security and privacy protection with respect to untrusted storage network has become a formidable challenge in research. To address these challenges, in this paper we propose a self-destruction system, named CloudSky, which is able to enforce the security of user privacy over the untrusted cloud in a controllable way. CloudSky exploits a key control mechanism based on the attribute-based encryption (ABE) and takes advantage of active storage networks to allow the user to control the subjective life-cycle and the access control polices of the private data whose integrity is ensured by using HMAC to cope with untrusted environments. %and thereby adapting it to the cloud in terms of both performance and security requirements. The feasibility of the system in terms of its performance and scalability is demonstrated by experiments on a real large-scale storage network.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".