Fine-Grained Query Authorization With Integrity Verification Over Encrypted Spatial Data in Cloud Storage
Bibliographic record
Abstract
In this article, a fine-grained query authorization scheme with integrity verification is proposed over encrypted spatial data for location-based services (LBS). The fine-grained query authorization is enabled based on a distribution of the spatial data by employing a non-uniform partition in the spatial domain to generate a density-based space filling curve (DSC), which can be used to generate index values for querying and transformation keys. The transformation keys can be used to generate query tokens for a secure spatial query as well as construct a transformation key tree whose subtree can be distributed by the LBS provider to an authorized user as transformation key for query tokens generation. Furthermore, the proposed scheme constructs a Merkle quad tree (MQ-tree) to support integrity verification by aggregating a digest of the spatial data based on the DSC and employing the MQ-tree as a verification structure. The LBS provider can share a subtree of the MQ-tree to authorized user as his verification structure, which corresponds to the transformation key of the authorized user. In this way, the authorized user can only generate the valid query tokens and verify the query results in his authorized region. The security properties of the proposed scheme is discussed, and extensive experimental results demonstrate the high efficiency of verification structure generation and verification operations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.003 |
| 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".