Towards Auditable and Privacy-Preserving Online Medical Diagnosis Service Over Cloud
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While online medical diagnosis provides significant convenience to users, it also incurs the risk of privacy breaches, which inspired the emergence of various privacy-preserving online medical schemes. Nonetheless, existing schemes either compromise partial privacy to third parties or rely on cryptographic methods with high computational complexity. In particular, they do not anticipate user’s disputes to the extent that there is no audit process to guarantee the correctness of the diagnosis results and the fairness of the schemes. Consequently, we propose an efficient and privacy-preserving online medical diagnosis scheme based on additive secret sharing (ASS). First, the anonymity of the user is provided in the medical diagnosis process, which ensures that the cloud cannot link the diagnosis results to the user. Then, we devise a minimum value protocol and a range comparison protocol to enhance the security of the online diagnosis. In addition, considering user’s disputes that arise in realistic scenarios (e.g., malicious users may cheat the diagnosis system for personal benefits), we construct a blockchain-based audit process to detect user’s behaviors and settle controversies. Finally, we demonstrate the security and efficiency of the proposed scheme with theoretical analysis and experimental evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it