Google's HEIR Aims to Make Homomorphic-Encrypted Inference a One-Click Capability

8.1 relevance

Google's HEIR for homomorphic encryption inference is cutting-edge, deeply technical, and relevant to secure AI/ML infrastructure.

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Why It Matters

For engineers building privacy-sensitive AI services or deploying models on untrusted infrastructure, HEIR offers a path to encrypted inference without redesigning models, though the performance trade-offs demand careful evaluation against alternatives like local execution.

Key Takeaways

Evaluate HEIR's benchmark code for your model's operation profile (e.g., LLMs vs. branching-heavy tasks) before committing to homomorphic encryption for production inference.

Google's HEIR Aims to Make Homomorphic-Encrypted Inference a One-Click Capability

Summary

Google open-sourced HEIR, a compiler toolchain that compiles pre-trained PyTorch models to operate on homomorphically encrypted data, enabling private inference without exposing inputs or proprietary models. HEIR uses an intermediate representation to abstract complex operations across dialects, but performance remains a key concern: community benchmarks show 1000x overhead for basic operations, though LLMs may fare better due to reliance on addition and multiplication over branching. Google has not yet published HEIR's relative speed for LLMs, and the current workflow requires manual steps via torch_mlir to export models to MLIR.

Author

Sergio De Simone