Achieving Efficient and Privacy-Preserving Range Query in Fog-enhanced IoT with Bloom Filter
Bibliographic record
Abstract
Fog-enhanced Internet of Things (IoT), which can locally process data at the network edge for better response to the IoT field and pre-computation for further efficient process at the cloud side, has attracted substantial studies in recent years. However, as the fog device is not fully trustable at the network edge, more advancement in efficiency and privacy should be considered to persuade enterprises to migrate to fog and cloud environments. With this in mind, in this paper, we propose a new communication-efficient privacy-preserving range query in the fog-enhanced IoT. The proposed scheme is characterized by employing Paillier homomorphic cryptosystem and ingenious Bloom filter data structure for simultaneously achieving better privacy and higher efficiency in the count aggregation in a privacy-preserving range query scenario. More precisely, $(n+|E|)\log n$-bit communication efficiency can be achieved by our proposed scheme where $n, |E|$ are respectively the range size and the ciphertext size. Detailed security analysis shows that our proposed scheme really achieves the privacy preservation in the range query. Extensive experiments are conducted, and the results demonstrate the efficiency of our proposed scheme.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".