tf-encrypted/tf-encrypted

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1243 stars · 212 forks observed · 2026-08-28

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

TF Encrypted is a Python framework for privacy-preserving machine learning in TensorFlow, enabling training and prediction on encrypted data using secure multi-party computation and homomorphic encryption. It exposes a Keras-like API so developers can build encrypted ML workflows without cryptography expertise.

Use cases

• train machine learning models on encrypted data
• run predictions on private data without decrypting it
• build privacy-preserving ML services with secure multi-party computation
• apply homomorphic encryption to TensorFlow models
• protect sensitive data in collaborative ML across parties
• experiment with encrypted deep learning without cryptography expertise

When to choose

• you need to train or run inference on data that must stay encrypted
• you want privacy-preserving ML with a familiar TensorFlow/Keras-style API
• multiple parties need to jointly compute on private data without sharing it

When to avoid

• you need cutting-edge performance or the latest MPC research techniques, as the project is in maintenance mode
• you are not using TensorFlow or need framework-agnostic encrypted computation
• you rely on legacy TF1 features like sessions and placeholders

Facets

framework · maturity maintenance

machine-learning deep-learning cryptography privacy security machine-learning privacy security deep-learning python cross-platform secure-multi-party-computation homomorphic-encryption privacy-preserving-ml tensorflow encrypted-computation confidential-computing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="tf-encrypted/tf-encrypted")