Secure and Efficient <i>k</i> NN Classification for Industrial Internet of Things
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The k-nearest neighbors (kNN) classification has been widely used for defective product identification and anomaly detection in the Industrial Internet of Things (IIoT). In this article, we propose a secure and efficient distributed kNN classification algorithm (SEED-kNN) to prevent information and control flow exposure while supporting large-scale data classification on distributed servers. Specifically, we first design a secure and efficient vector homomorphic encryption (VHE) scheme by constructing a key-switching matrix and a noise matrix for data encryption. Based on the designed VHE, SEEDkNN is proposed to efficiently achieve the confidentiality of data flow, kNN query, and class label, while enabling homomorphic operations on the encrypted data. Moreover, by leveraging the Map/Reduce architecture, SEED-kNN enables the kNN classification over the large-scale encrypted data on distributed servers for industrial control systems. Finally, we demonstrate that SEEDkNN achieves semantic security and high classification accuracy, and is applicable in IIoT due to its high efficiency.
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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 | 0.000 |
| 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 | 0.001 | 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