End-to-end encrypted ML inference with Amazon SageMaker AI and FHE

2026-06-08 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Cybersecurity & Data Privacy · Depth: Advanced, long

Summary

Machine learning (ML) inference often requires processing sensitive data, necessitating solutions that keep information encrypted throughout the entire process. This post details how to implement end-to-end encrypted ML inference using Amazon SageMaker AI and Fully Homomorphic Encryption (FHE) with the `concrete-ml` library (version 1.9.0) and `concrete-python` (version 2.10.0). The approach involves training an FHE-enabled model in SageMaker AI via a custom container, deploying it to an asynchronous inference endpoint, and utilizing a custom client to encrypt queries and decrypt predictions. This ensures queries, responses, and intermediate values remain unreadable by observers, including SageMaker AI itself, addressing privacy concerns in sectors like healthcare, energy, and telecommunications. While FHE introduces significant performance overheads, up to 100,000X slowdown, this can be reduced to 2800X with quantization or 500X on `ml.m5.24xlarge` instances, making it practical for asynchronous or batch workloads.

Key takeaway

For ML Engineers deploying models with sensitive data, consider FHE with Amazon SageMaker AI and `concrete-ml` to maintain end-to-end encryption. This approach ensures data privacy from the cloud provider, crucial for healthcare or telecommunications. Be aware of the substantial performance overhead, potentially 500X even with optimizations like quantization and `ml.m5.24xlarge` instances. Prioritize asynchronous or batch workloads where latency is less critical. Implement robust IAM roles and S3 encryption for comprehensive security.

Key insights

FHE enables ML inference on encrypted data, ensuring privacy from cloud providers like SageMaker AI.

Principles

• FHE allows computation on encrypted data without decryption.
• Security relies on mathematics, not hardware isolation.
• FHE introduces significant performance overheads.

Method

Train an FHE-enabled `concrete-ml` model in SageMaker AI with a custom container. Deploy to an async inference endpoint. Clients encrypt queries, upload to S3, send S3 locations to endpoint, then decrypt S3-stored predictions.

In practice

• Use `concrete-ml` for FHE-based ML inference.
• Quantize models to mitigate FHE performance overhead.
• Implement custom SageMaker AI containers for FHE training/inference.

Topics

• Fully Homomorphic Encryption
• Amazon SageMaker AI
• concrete-ml Library
• Encrypted ML Inference
• Data Privacy
• AWS Cloud Security

Code references

• aws-samples/sample-end-to-end-encrypted-ml-inference-with-amazon-sagemaker-ai-and-fhe

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.