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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".