Matrix Gaussian Mechanisms for Differentially-Private Learning
Bibliographic record
Abstract
The wide deployment of machine learning algorithms has become a severe threat to user data privacy. As the learning data is of high dimensionality and high orders, preserving its privacy is intrinsically hard. Conventional differential privacy mechanisms often incur significant utility decline as they are designed for scalar values from the start. We recognize that it is because conventional approaches do not take the data structural information into account, and fail to provide sufficient privacy or utility. As the main novelty of this work, we propose <i>Matrix Gaussian Mechanism</i> (MGM), a new <inline-formula><tex-math notation="LaTeX">$ (\epsilon,\delta)$</tex-math></inline-formula> -differential privacy mechanism for preserving learning data privacy. By imposing the unimodal distributions on the noise, we introduce two mechanisms based on MGM with an improved utility. We further show that with the utility space available, the proposed mechanisms can be instantiated with optimized utility, and has a closed-form solution scalable to large-scale problems. We experimentally show that our mechanisms, applied to privacy-preserving federated learning, are superior than the state-of-the-art differential privacy mechanisms in utility.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".