tensorflow/privacy

Health v2 · maintenance only

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2026 stars · 475 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TensorFlow Privacy is a Python library providing TensorFlow optimizers for training machine learning models with differential privacy. It includes tutorials and analysis tools for computing the privacy guarantees of trained models.

Use cases

• train a neural network with differential privacy guarantees
• add DP-SGD gradient clipping to a Keras model
• compute the epsilon privacy budget of a training run
• test models for empirical privacy leakage
• train models on sensitive data like medical records

When to choose

• you train TensorFlow models on sensitive or personal data and need formal privacy guarantees
• you need membership inference analysis or privacy accounting tools
• you want DP optimizers that integrate with existing Keras workflows

When to avoid

• you use PyTorch or JAX rather than TensorFlow (consider Opacus instead)
• you need privacy guarantees for inference or data release rather than model training
• you need production-grade DP training with minimal overhead on complex architectures

Facets

library · maturity active

machine-learning privacy security llm-training machine-learning privacy deep-learning python differential-privacy tensorflow dp-sgd gradient-clipping privacy-accounting

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For agents

markdown · JSON · MCP: product_card(name="tensorflow/privacy")