On the Privacy Guarantees of Differentially Private Stochastic Gradient Descent
Bibliographic record
Abstract
Differentially Private Stochastic Gradient Descent (DP-SGD) is a widely adopted algorithm for privately training machine learning models. An inherent feature of this algorithm is the incorporation of gradient clipping to counteract the influence of individual samples during training. Nevertheless, the introduction of gradient clipping also introduces non-convexity into the problem, rendering it challenging to derive upper bounds on the privacy loss. In this paper, we establish effective upper bounds for the privacy loss of both projected DP-SGD and regularized DP-SGD, without relying on convexity or smoothness assumptions regarding the loss function. Our approach involves a direct analysis of the hockey-stick divergence between coupled stochastic processes through the application of nonlinear data processing inequalities.
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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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".