Privacy-Preserving Collaborative Model Learning Scheme for E-Healthcare
Bibliographic record
Abstract
With the advances of data mining and the pervasiveness of cloud computing, online medical diagnosis service has been extensively applied in e-heathcare field, and brought great conveniences to people's life. However, due to the insufficient data sharing among healthcare centers under the security and privacy concerns of medical information, the flourish of online medical diagnosis service still faces many severe challenges including diagnostic accuracy issues. In this paper, in order to address the security issues and improve the accuracy of online medical diagnosis service, we propose a new privacy-preserving collaborative model learning scheme with skyline computation, called PCML. With PCML, healthcare centers can securely learn a global diagnosis model with their local diagnosis models in the assistance of cloud, and the sensitive medical data of each healthcare center is well protected. Specifically, with a secure multi-party vector comparison algorithm (SMVC), all local diagnosis models are encrypted by their owners before being sent to the cloud, and can be directly operated without decryption. Detailed security analysis shows that PCML can resist security threats in the semi-honest model. Moreover, PCML is implemented with medical datasets from UCI machine learning repository, and extensive simulation results demonstrate that PCML is efficient and can be implemented effectively.
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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 | 0.010 |
| 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 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".