Practical Privacy-preserving High-order Bi-Lanczos in Integrated Edge-Fog-Cloud Architecture for Cyber-Physical-Social Systems
Why this work is in the frame
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Bibliographic record
Abstract
Smart environments, also referred to as cyber-physical-social systems (CPSSs), are expected to significantly benefit from the integration of edge, fog, and cloud for intelligence service flexibility, efficiency, and cost saving. High-order Bi-Lanczos method has emerged as a powerful tool serving as multi-dimensional data processing, such as prevailing feature extraction, classification, and clustering of high-order data, in CPSSs. However, integrated edge-fog-cloud architecture is open and users have very limited control; how to carry out big data processing without compromising the security and privacy is a challenging issue in edge-fog-cloud-assisted smart applications. In this work, we propose a novel and practical privacy-preserving high-order Bi-Lanczos scheme in integrated edge-fog-cloud architectural paradigm for smart environments. More precisely, we first propose a privacy-preserving big data processing model using the synergy of edge, fog, and cloud. The proposed model enables edge, fog, and cloud to cooperatively complete big data processing without compromising users’ privacy for large-scale tensor data in CPSSs. Subsequently, making use of the model, we present a privacy-preserving high-order Bi-Lanczos scheme. Finally, we theoretically and empirically analyze the security and efficiency of the proposed privacy-preserving high-order Bi-Lanczos scheme based on an intelligent surveillance system case study. And the results demonstrate that the proposed scheme provides a privacy-preserving and efficient way of computations in integrated edge-fog-cloud paradigm for smart environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 it