Privacy-preserving synthetic image data generation and classification
Bibliographic record
Abstract
Computer vision, generative models (e.g., ChatGPT, etc.), and deep learning are now widely used across various sectors, from large corporations to end devices, simplifying people’s lives and improving the reliability of medical findings. Sensitive image data and deep learning’s high memorization capacity pose privacy risks, particularly for medical images containing sensitive private information. De-anonymization does not work due to the re-identification risk and reduced utility. So, we developed a differentially private approach with selective noise in addition to generating high-dimensional synthetic medical image data with guaranteed differential privacy. In addition to ensuring data privacy, protecting the classification model’s privacy is crucial due to its vulnerability to “membership inference attacks.” State-of-the-art (e.g., differential privacy, etc.) defenses compromised task accuracy to preserve privacy, and some methods reuse private data or require more public data, which is impractical in some domains. To address privacy concerns while maintaining utility, we propose a collaborative distillation approach that transfers knowledge using minimal synthetic data, resulting in a compact private classifier model.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".