TF Encrypted

TF Encrypted is an open-source library built on TensorFlow that enables privacy-preserving machine learning using secure multi-party computation (MPC) and homomorphic encryption. It allows developers to train and run machine learning models on encrypted data without exposing sensitive information, making it suitable for applications where data privacy is critical. The library integrates seamlessly with TensorFlow workflows, providing familiar APIs while handling the underlying cryptographic operations.

Also known as: TF-Encrypted, TFEncrypted, TensorFlow Encrypted, TFE, tf-encrypted

Why learn TF Encrypted?

Developers should learn TF Encrypted when working on machine learning projects that involve sensitive data, such as in healthcare, finance, or government sectors, where privacy regulations like GDPR or HIPAA apply. It is particularly useful for federated learning scenarios, secure data collaborations between multiple parties, and any application where model training must occur on encrypted datasets to prevent data breaches. By using TF Encrypted, developers can build AI systems that maintain confidentiality while leveraging the power of TensorFlow.

Learning Resources

• docs: TF Encrypted Documentation
• docs: GitHub Repository
• tutorial: Introduction to TF Encrypted Tutorial
• video: Privacy-Preserving ML with TF Encrypted (Video)
• course: Secure and Private AI Course by OpenMined