An Efficient Privacy-preserving Logistic Regression Scheme for Aging-in-place Systems
Bibliographic record
Abstract
With the global demographic trend showing an increase in the elderly population, there is a pressing demand for innovative approaches to monitor and enhance the quality of life for this segment. In response to the growing need for advanced healthcare solutions for the aging population, this study presents an efficient privacy-preserving logistic regression scheme for aging-in-place to improve the safety and well-being of elderly individuals significantly. Furthermore, in light of increasing cybersecurity threats and the sensitivity of health data, the scheme introduces a novel zero-sum method, as well as matrix encryption. These measures are designed to secure users’ health data and safeguard the logistic regression model’s vital parameters against unauthorized access. The combination of predictive analytics and data security protocols offers a comprehensive solution to support elderly care, making significant strides toward ensuring the privacy and protection of personal health information. This work is pivotal in enhancing elderly care through innovative technology and robust data security.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".