Google's HEIR Aims to Make Homomorphic-Encrypted Inference a One-Click Capability
Google just open-sourced the compiler that turns any pre-trained model into encrypted-by-default. No rewrites needed.
Why it matters
Homomorphic encryption—compute-on-encrypted-data—has been a theoretical win for a decade. HEIR makes it a practical deployment option for practitioners building privacy-critical AI systems, lowering the barrier from research curiosity to production tooling.
The key facts
10 to knowHEIR is an open-source compiler and development toolchain
Can compile pre-trained AI models to operate on encrypted data without modification
Homomorphic encryption allows computation on encrypted inputs without decryption
Targets ease of deployment ('one-click capability')
Published August 2026 by Google
Google releases HEIR: an open-source compiler and development toolchain
HEIR compiles pre-trained models to operate on encrypted data without decryption
Targets ease-of-deployment for homomorphic encryption in inference workflows
Date: August 2026
Source: Google (open-source release)
Go to the source
InfoQ AI/MLinfoq.com
Publisher excerpt: Google is introducing HEIR (Homomorphic Encryption Intermediate Representation), an open-source compiler and development toolchain designed to make encrypted computation easier to deploy. In particular, HEIR can compile pre-trained AI models built for conventional, unencrypted inputs so they can…