Multi-Client Boolean File Retrieval With Adaptable Authorization Switching for Secure Cloud Search Services
Bibliographic record
Abstract
Secure cloud search services provide a cost-effective way for resource-constrained clients to search encrypted files in the cloud, where data owners can customize search authorization. Despite providing fine-grained authorization, traditional attribute-based keyword search (ABKS) solutions generally support single keyword search. Towards expressive queries over encrypted data, multi-client searchable symmetric encryption (MC-SSE) was introduced. However, current search authorizations of existing MC-SSEs: (i) cannot support dynamic updating; (ii) are (semi-)black-box implementations of attribute-based encryption; (iii) incur significant cost during system initialization and file encryption. To address these limitations, we present AasBirch, an MC-SSE system with fast fine-grained authorization that supports adaptable authorization switching from one policy to any other one. AasBirch achieves constant-size storage and lightweight time cost for system initialization, file encryption and file searching. We conduct extensive experiments based on Enron dataset in real cloud environment. Compared to state-of-the-art MC-SSE with fine-grained authorization, AasBirch achieves 30$\sim 200\times$smaller public parameter and secret key size, with the assumed least frequent keyword in a query ($s$-term) as 21. Moreover, it runs 10$\sim 20\times$faster for file encryption and$>20\times$faster for file searching. In addition, AasBirch outperforms 80,000× (resp. 7,850×) faster with$s$-term=1 (resp. =21), as compared to classic dynamic ABKS system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".