Google Research announces VaultGemma, the largest open language model (LLM) trained from scratch under differential privacy, marking a major step forward in combining strong privacy protections with high utility in AI systems.
VaultGemma embodies Google’s commitment to building AI with privacy at its core. Differential privacy (DP) is a mathematically rigorous technique that protects against memorization by adding calibrated noise. Google reports that while DP introduces trade-offs such as reduced training stability and increased computation and batch size costs it is essential for trustworthy AI deployment.
Drawing on a new research partnership with Google DeepMind, Google has published “Scaling Laws for Differentially Private Language Models,” which provides a detailed framework for modelling the trade-offs among compute, privacy, and utility. These scaling laws enabled the team to determine optimal configurations of model size, batch size, iterations, and noise levels to train VaultGemma, a 1-Billion-parameter model, under differential privacy. Google is releasing VaultGemma’s weights via Hugging Face and Kaggle, along with a technical report, to support further development in private AI.
Key Findings & Innovations
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The research establishes that the “noise-batch ratio” the amount of DP noise relative to batch size is a central factor determining performance. Empirical studies show that greater batch sizes, more iterations, or larger data budgets can partially offset the overhead of privacy noise.
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Optimal DP training configurations often involve smaller models than those used without DP, but with significantly larger batch sizes. This insight, while known in theory to differential privacy experts, is now validated with concrete scaling laws that offer practical guidance.
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The team adopted Poisson sampling within the DP-SGD (Differentially Private Stochastic Gradient Descent) framework. To maintain strong privacy guarantees while keeping computation feasible, they addressed challenges arising from variable batch sizes and randomized data ordering via recent advances in scalable DP-SGD methods.
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Performance & Privacy Guarantees
VaultGemma was field-tested in comparison with its non-private counterpart (Gemma 3 at 1B parameters) as well as a similar-sized GPT-2 baseline. The results demonstrate that current differential privacy training methods yield utility comparable to non-private models from approximately five years ago.
The model was trained with a sequence-level privacy guarantee of ε ≤ 2.0 and δ ≤ 1.1e-10, where each sequence consists of 1,024 consecutive tokens tokenized from heterogeneous data sources. Sequence-level DP ensures that any single training sequence has bounded influence on the final model. When the model was tested for memorization (by prompting with a 50-token prefix from the training data), VaultGemma showed no detectable memorization of its training data.
Official Comment
“VaultGemma represents a significant step forward in the journey toward building AI that is both powerful and private by design,” said Google Research. The company states that although there remains a utility gap between DP-trained and non-DP-trained models, that gap can be systematically narrowed with further research into mechanism design for differential privacy training. Google hopes that releasing VaultGemma and its research will empower the broader community to build the next generation of safe, responsible, and private AI for everyone.
