A Platform for Secure, Privacy-Preserving Machine Learning

Build better models by working with more valuable data

Self-serve access to protected data

Integrated with your existing workflow

Powered by modern privacy techniques

Governed by security policies and permissions

From data access to model prototyping and deployment,

PrivacyAI helps you manage the complex relationship between data privacy and data science.

Protect the privacy of
individuals in a dataset

Combine data from multiple parties while maintaining confidentiality

Train models and run
predictions on encrypted data

Maintain your existing workflow

PrivacyAI integrates seamlessly with existing frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, NumPy, and Pandas so you can keep your existing code.

Unlock access to higher quality data

Build larger, more representative datasets by unlocking data silos within your company, across partners, and even across competitors.

Use modern privacy techniques

PrivacyAI is powered by techniques such as differential privacy, federated learning, remote execution, secure multi-party computation, and homomorphic encryption

Backed by an active open source community

PrivacyAI is built by the core contributors of open source projects such as
PySyft, TF Encrypted, and TF Trusted

Ready to learn more? Request a demo