Amazon SageMaker AI now supports fully homomorphic encryption (FHE) to enable end-to-end encrypted machine learning (ML) inference. This approach allows data to remain encrypted throughout the entire ML inference process, ensuring it remains unreadable by observers, including the cloud infrastructure itself. The integration of FHE with SageMaker AI enables organizations to run ML models on sensitive data without exposing it to third parties.
Using FHE, encrypted data can be processed without decryption, allowing models to generate encrypted predictions. This capability is particularly valuable in scenarios such as healthcare, where patient data must remain private, or in the energy sector, where satellite images of politically sensitive locations should not be exposed. The solution uses the concrete-ml library, which supports various model types and is API-compatible with scikit-learn, offering a more flexible and high-level approach compared to previous methods.
The blog post outlines a step-by-step guide for training and deploying a model using concrete-ml in SageMaker AI, including creating a custom container, deploying the model to an inference endpoint, and building a client for encrypted queries. This system ensures that data remains encrypted from the client to the model and back, providing a secure and scalable solution for privacy-sensitive applications.
Source: awsml
