Privacy-Preserving Logistic Regression Prediction over Vertically Partitioned Data for AiP System
Bibliographic record
Abstract
Aging in Place (AiP) programs enable elderly individuals to live independently and comfortably within their homes and communities by utilizing technological innovations such as smart homes and remote healthcare monitoring. In practical AiP scenarios, data necessary for accurate health predictions are typically vertically partitioned across multiple medical institutions, raising significant privacy concerns during data integration and analysis. To address this challenge, we propose an efficient and privacy-preserving logistic regression (LR) prediction scheme tailored explicitly for vertically partitioned AiP data. Our scheme effectively combines the computational efficiency of Trusted Execution Environments (TEE) under the honest-but-curious model with cryptographic security based on the Matrix Diffie-Hellman (MDDH) assumption. Security analysis confirms that our approach provides privacy protections against honest-but-curious adversaries. Extensive experimental evaluations demonstrate that our proposed scheme achieves computational efficiency, privacy protection, and practical scalability for real-world AiP implementations.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".