Enabling Encrypted Boolean Queries in Geographically Distributed Databases
Bibliographic record
Abstract
The persistent growth of big data applications has being raising new challenges in managing large volumes of datasets with high scalability, confidentiality protection, and flexible types of search queries. In this paper, we propose a secure design to disassemble the private dataset with the aim to store them across geographically distributed servers while supporting secure multi-client Boolean queries. In this design, the data owner encrypts the private database with the searchable index attributes. The encrypted dataset will be disassembled and distributed evenly across multiple servers by leveraging the property of a distributed index framework. By constructing an encryption structure, generating search tokens, and enabling parallel query, we show how the proposed design performs the secure while efficient Boolean search. These queries are not only limited to those initiated by the data owner but also can be extended to support multiple authorized clients, where each client is allowed to access a necessary part of the private database. In this stage, we advocate a non-interactive authorization scheme where data owner is not required to stay online to process the query request. Moreover, the query operation can be executed in parallel, which significantly improves the search efficiency. We formally characterize the leakage profile, which allow us to follow the existing security analysis method to demonstrate that our system can guarantee data confidentiality and query privacy. To validate our protocol, we implement a system prototype and evaluate the efficiency of our construction. Through experimental results, we demonstrate the effectiveness of our protocol in terms of data outsourcing time and Boolean query time.
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 | 0.003 | 0.007 |
| 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 | 0.002 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".