Secure Multi-User k-Means Clustering Based on Encrypted IoT Data
Bibliographic record
Abstract
IoT technology collects information from a lot of clients, which may relate to personal privacy. To protect the privacy, the clients would like to encrypt the raw data with their own keys before uploading. However, to make use of the information, the data mining technology with cloud computing is used for the knowledge discovery. Hence, it is an emergent issue of how to effectively performing data mining algorithm on the encrypted data. In this paper, we present a k-means clustering scheme with multi-user based on the IoT data. Although, there are many privacy-preserving k-means clustering protocols, they rarely focus on the situation of encrypting with different public keys. Besides, the existing works are inefficient and impractical. The scheme we propose in this paper not only solves the problem of evaluation on the encrypted data under different public keys but also improves the efficiency of the algorithm. It is semantic security under the semi-honest model according to our theoretical analysis. At last, we evaluate the experiment based on a real dataset, and comparing with previous works, the result shows that our scheme is more efficient and practical.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".