DeepMind Blog
Oct 23, 2025
VaultGemma: The world's most capable differentially private LLM
We introduce VaultGemma, the most capable model trained from scratch with differential privacy.
Creating Privacy Preserving AI with Substra
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Security and Privacy in Agentic AI: Grand Challenges and Future Directions
arXiv:2607. 06608v1 Announce Type: cross Abstract: We present key challenges and future research directions in the security and privacy of agentic AI, based on a horizon-scanning exercise that brought together thirty leading international experts from academia, industry, and government to engage in focused discussions and collaborative exercises on the emerging risks associated with the growing agency of AI.
arXiv Machine Learning
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Performative Privacy: When Differential Privacy Maximizes Utility
arXiv:2608.28198v1 Announce Type: new Abstract: Privacy-preserving learning is often motivated by the idea that protecting users' data can preserve trust and thus participation, improving utility in...
arXiv Machine Learning
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Revisiting the Provable-Auditable Privacy Gap of DP-SGD
arXiv:2608.28934v1 Announce Type: new Abstract: Differential privacy (DP) has traditionally been used to provide theoretical upper bounds on an algorithm's stability to changing its training data. In...
arXiv Machine Learning
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DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data
arXiv:2606. 04399v1 Announce Type: new Abstract: In the paradigm of decentralized learning, a group of agents collaborate to train a global model using distributed datasets without a central server.
arXiv AI
Jul 13
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
arXiv:2512. 03238v2 Announce Type: replace-cross Abstract: High quality data is needed to unlock the full potential of AI for end users.
arXiv Machine Learning
Jul 1
Efficient Public Verification of Private ML via Regularization
arXiv:2512. 04008v2 Announce Type: replace Abstract: Training with differential privacy (DP) guarantees dataset members that they cannot be identified by users of the released model.
arXiv AI
Jul 2
NeuroFilter: Activation-Based Guardrails for Privacy-Conscious LLM Agents
arXiv:2601. 14660v2 Announce Type: replace-cross Abstract: Agentic Large Language Models (LLMs) are models able to reason, plan, and execute tools over unstructured data.
arXiv AI
Aug 18
Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy
arXiv:2509. 10691v3 Announce Type: replace-cross Abstract: Decentralized federated learning enables collaborative model training without a central server, but shared model updates can still leak sensitive information through inversion, reconstruction, and membership inference attacks.
arXiv Machine Learning
Jun 16
DP-Hype: Federated Differentially Private Hyperparameter Search
arXiv:2510. 04902v3 Announce Type: replace Abstract: Tuning hyperparameters in federated machine learning can substantially impact model performance.
arXiv AI
Sep 4
PrivateHub: Contrastive Diffusion Model for Private Sensor-Intensive Environment Data Generation
PrivateHub is a contrastive diffusion model designed to generate synthetic multi‑sensor data that protects private user activities while keeping non‑private applications detectable. It operates in two stages: App‑Conditioned Pre‑training, which conditions the model on application embeddings, and App‑Aware Fine‑tuning, which uses contrastive learning to separate private from non‑private data. Experiments on three real‑world datasets demonstrate that PrivateHub reduces private‑application inference accuracy by 40–50% without harming non‑private performance and remains robust even when attackers retrain on the synthetic data.
arXiv AI
Jul 22
Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data
arXiv:2606. 16952v2 Announce Type: replace-cross Abstract: The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets.