A Privacy-Preserving Multidimensional Range Query Scheme for Edge-Supported Industrial IoT
Bibliographic record
Abstract
Edge-supported Industrial Internet of Things (IIoT) has recently received significant attention since the edge computing can greatly improve the service quality of IIoT applications. However, edge servers are not fully trusted and are often deployed at the edge of the network. Therefore, there are some security challenges that need to be addressed. For edge-supported IIoT, a privacy-preserving range query is one of the most important functional requirements. Recently, some privacy-preserving range query solutions have been proposed in different fields. However, most of them only support single-dimensional range query, which are inefficient for the requirement of multidimensional range query. To address these problems, we propose a privacy-preserving multidimensional range query scheme for edge-supported IIoT, called Edge-PPMRQ, in this article. In Edge-PPMRQ, a novel range division algorithm is designed, through which the multidimensional ranges can be merged into one range, so as to achieve multidimensional range query through one query request. In addition, Edge-PPMRQ also supports the range queries for continuous, discontinuous, and arbitrary boundary ranges. The detailed security analysis proves that Edge-PPMRQ is privacy preserving for the query ranges, the query results, and the sensed data of IIoT devices. Furthermore, extensive comparison experiments also illustrate that Edge-PPMRQ is efficient in communication and computation.
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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 | 0.002 | 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 | 0.001 | 0.005 |
| 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".