HEIR homomorphic encryption compiler update: compiling pre-trained ML models for fully private inference
jeremyjkun · x · 2026-09-05
Jeremy Kun, author of the Google Security Blog post on HEIR, publishes a companion deep-dive with no word-count limits. HEIR is a compiler that converts programs to run directly on encrypted data: assuming the crypto holds, the executing machine learns zero bits about inputs, outputs, or intermediates — enabling perfectly private ML inference.
The blog post focuses on compiling pre-trained models, with four small but nontrivial compiled examples tied to a clonable GitHub repo (the main hurdle is installing bazel). Kun also outlines his view of the project roadmap and points to heir.dev docs and his ASPLOS talk.
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