Simple Approximations for Fast and Secure Deep Learning on Genomic Data
Bibliographic record
Abstract
State-of-the-art frameworks for privacy-preserving artificial neural networks often rely on secret sharing to protect sensitive data. Unfortunately, operating on secret shared data complicates a number of non-linear functions that are central to deep learning, such as batch normalization and rectified linear units (ReLUs). We offer simple procedures for approximating these non-linear operations. The approximations we propose significantly reduce the training runtime of a privacy-preserving convolutional neural network (CNN) that we designed to diagnose breast cancer from secret shared gene expression profiles. In just over five minutes of training, our approximation-based privacy-preserving CNN achieves an average test accuracy of 96%. When we apply an exact garbled circuit solution for the ReLU function, we find that the privacy-preserving model requires days of computation to achieve the same level of accuracy. The dramatic improvement in training runtime yielded by our ReLU approximation may prove useful for other medical applications of privacy-preserving neural networks.
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How this classification was reachedexpand
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 | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.016 | 0.076 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".