3-Multi ranked encryption with enhanced security in cloud computing
Bibliographic record
Abstract
Searchable Encryption (SE) enables data owners to search remotely stored ciphertexts selectively. A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users, and even return the top-k most relevant search results when requested. We refer to a model that satisfies all of the conditions a 3-multi ranked search model. However, SE schemes that have been proposed to date use fully trusted trapdoor generation centers, and several methods assume a secure connection between the data users and a trapdoor generation center. That is, they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords, but it will never attempt to use it in any other manner than that requested, which is impractical in real life. In this study, to enhance the security, we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security assumptions. The proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center, thereby preventing attackers from determining whether two different queries include the same keyword. Moreover, we develop a method for managing multiple encrypted keywords from every data owner, each encrypted with a different key. Our evaluation demonstrates that, despite the trade-off overhead that results from the weaker security assumption, the proposed scheme achieves reasonable performance compared to extant schemes, which implies that our scheme is practical and closest to real life.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".