An Accurate and Privacy-Preserving Retrieval Scheme Over Outsourced Medical Images
Bibliographic record
Abstract
With the rapid advancement in medical imaging techniques, Content-Based (medical) Image Retrieval (CBIR), which can assist in disease diagnosis, has gained much attention in both academia and industry. However, due to patients’ sensitive information involved in medical images, privacy-preserving CBIR is a challenge worth exploiting. Though several privacy-preserving CBIR schemes have been put forth, they can only resist known-background attack (KBA), and do not suffice for protecting the image privacy in outsourced settings. In this article, aiming at the above challenge, we first design a novel Privacy-preserving Mahalanobis Distance Comparison (PMDC) method to improve the accuracy of medical images retrieval. Then, combined with the Mahalanobis distance based Fuzzy C-Means (FCM-M) algorithm, a scheme named TAMMIE is proposed to achieve accurate and privacy-preserving medical image retrieval over encrypted data. With TAMMIE, an image owner can securely outsource the images and indexes to a cloud server, and query users can request retrieval services from the cloud server while keeping their queries private. Detailed security analysis shows that our proposed schemes are secure under the attack stronger than KBA. Furthermore, thorough empirical experiments conducted on two real-world and one synthetic datasets also demonstrate the efficiency of TAMMIE.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".