Bibliographic record
Abstract
Despite the promise of scalability, efficiency and low cost, the rapid adoption of cloud computing also raises many concerns. In particular, security and privacy has been at the forefront of discussions and research in recent years. In this paper, we consider the unexplored problem of malware detection on encrypted cloud storage, especially relevant when cloud storage facilities are used for data archiving and back up. We noted disadvantages of the current approach to malware detection in anti-virus software, such as the frequent software updates and the threat of reverse engineering as highlighted by recent allegations of a potential sabotage by a industry partner. To address these issues, we propose solutions for performing malware detection in the following scenarios: A private malware scanner for encrypted cloud storage accessed solely by the data owner, an anti-virus as a service provider operating over encrypted cloud data, and an anti-virus service provider operating over unencrypted cloud services. Our private malware scanning solution is based on encrypted indexes and achieves performance comparable to leading keyword search algorithms. Our scheme for anti-virus as a service, based on homomorphic encryption, protects against malicious agents by performing a portion of the detection algorithm on an anti-virus server. The scheme also protects user privacy, having its entire scanning process performed in the encrypted domain. We also include a discussion on the merit of implementing anti-virus as a service on unencrypted data.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".