End-to-end encrypted ML inference with Amazon SageMaker AI and FHE
TL;DR
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.
Nauti's Take
FHE has long been the kind of security tech that shines in demos and vanishes from product roadmaps. If concrete-ml on SageMaker really maps cleanly onto the scikit-learn workflow, encrypted inference becomes less of a research project and more of an architecture choice for regulated teams.
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