An Efficient and Zero-Trust Approach for Privacy-Preserving Healthcare Diagnostics
Bibliographic record
Abstract
The integration of the Internet of Things (IoT) into the healthcare industry has led to the development of the Internet of Medical Things (IoMT). In IoMT, healthcare professionals diagnose and treat patients by analyzing data collected from medical sensors, with measurements transmitted via AI-powered mobile applications. These health records are subsequently processed through machine learning (ML) models for disease diagnosis$(\mathcal{DD})$and prediction. However, exposing such sensitive data raises privacy concerns, as it may lead to the inference of confidential patient information. To mitigate this issue, existing research primarily employs federated learning (FL)-based strategies to collaboratively train and generate an accurate global$\mathcal{DD}$model across multiple healthcare institutions. Despite these efforts, a significant gap remains in addressing privacy risks during the future$\mathcal{DD}$process in the deployment phase, once the global model has been established-an aspect that has not been thoroughly explored. In response, this paper introduces a novel decentralized privacy-preserving scheme for accurate$\mathcal{DD}$while ensuring patient data confidentiality. The proposed approach enables patients to encrypt their medical records using functional encryption (FE) without relying on a trusted key distribution center (KDC), thereby allowing$\mathcal{DD}$without exposing their health data. Additionally, we design a hybrid deep learning (DL) model to enhance diagnostic accuracy. To validate our approach, we evaluate its performance on real-world health records from the Cleveland dataset, sourced from the University of California Irvine (UCI). Our results demonstrate that the proposed scheme effectively diagnoses heart disease while maintaining robustness, preserving privacy, and minimizing computational overhead.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".