Achieving Privacy-Preserving Multi Dot-Product Query in Fog Computing-Enhanced IoT
Why this work is in the frame
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Bibliographic record
Abstract
Fog computing-enhanced IoT (Internet of Things), as it can provide better IoT services at the network edge, has received considerable attention in recent years. In this paper, for this new paradigm, we present a new privacy-preserving multi dot-product query scheme, called PMQ, which enables the control center to gain k dot-product results simultaneously in one query. Specifically, in the proposed PMQ scheme, the BGN homomorphic encryption is employed for encrypting query request and response, and a fog device is deployed at the network edge to assist the privacy- preserving k dot-product query. Detailed security analysis shows that the proposed PMQ can achieve better privacy preservation, i.e., no information in query request and response will be disclosed. In addition, extensive simulations are conducted, and the results demonstrate that the proposed PMQ scheme can achieve acceptable efficiency in terms of communication overheads and computational costs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| 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 it