End-to-end encrypted ML inference with Amazon SageMaker AI and FHE
Quick Answer
This paper shows that Amazon SageMaker now supports end-to-end encrypted machine learning inference using Fully Homomorphic Encryption (FHE) with the concrete-ml library.
Quick Take
Amazon SageMaker now supports end-to-end encrypted machine learning inference using Fully Homomorphic Encryption (FHE) with the concrete-ml library. 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.
Article Excerpt
From source RSS / original summaryThis 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 specifically for FHE-based inference. It supports several common types of models 'out of the box' and is even API compatible with the well-known ML library scikit-learn.
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