End-to-end encrypted ML inference with Amazon SageMaker AI and FHE
Quick Answer
Amazon SageMaker now supports end-to-end encrypted machine learning inference using Fully Homomorphic Encryption (FHE) with the concrete-ml library.
Quick Take
This high-level library simplifies FHE-based inference, offering compatibility with popular models and APIs like scikit-learn, enhancing flexibility and usability for developers.
Key Points
- Concrete-ml library enables flexible FHE-based ML inference with minimal setup.
- Supports various common model types directly, enhancing developer efficiency.
- API compatibility with scikit-learn allows easy integration with existing workflows.
- Previous implementations required low-level coding, now simplified for broader use.
Source Excerpt
This blog has previously discussed FHE for ML inference in the post Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing, but this post goes a little further. That previous post showed how to implement FHE-based inference 'from scratch' by hand-crafting a linear-regression algorithm using a low-level library called SEAL. Instead, this post shows a much more flexible and higher-level approach based on concrete-ml, a high-level library built specifi
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