Blind decryption for cloud computing
Bibliographic record
Abstract
Cloud computing is a tremendous opportunity for both enterprises and end users. Cloud users can benefit from the possibility of remote processing in order to outsource their data and infrastructure; that is why many companies have chosen to adopt cloud-deployed solutions such as remote databases, mail servers, and connected applications. However, the cloud fails to provide sufficient security measure to preserve the privacy of the data even from the service providers. Therefore, the research community has assigned to this topic the utmost attention by proposing several solutions like blind processing. This latter is based on homomorphic encryption schemes and allows performing operations on encrypted data without decryption. This way it is possible to protect users' privacy even from the cloud provider since we only publish an encrypted form of the sensitive data. But, most of the proposed solutions only deal with the case of a mono-setting environment where one user collaborates with one server, whereby the multi-user topology reveals another kind of issues such as key sharing and concurrent access. In this paper, we propose a blind decryption technique based on homomorphic encryption that allows not only a user but multiple users to manage the same encrypted data without sharing the secret key. The proposed protocol could be utilized to build various kinds of cloud applications which require the collaboration of several users like remote databases, files sharing, and video-on-demand services.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".