GoKawiilGoogle's HEIR project shows compiler for encrypted ML inference maturing
Google published a companion technical article detailing progress on HEIR, its open-source compiler that transforms programs so they run directly on encrypted data using homomorphic encryption. The team demonstrated compiling several small but functional pre-trained ML models, including a three-layer fraud-detection network, into homomorphically encrypted versions runnable via a public GitHub repository built with Bazel. The author also outlines internal workings and a roadmap for the project's future development.
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Homomorphic encryption lets a service run computations on sensitive data without ever seeing the underlying values, which could let cloud providers offer machine learning inference while guaranteeing zero visibility into user inputs or outputs. HEIR's progress toward compiling real, if small, ML models signals that fully private inference is edging closer to practical use rather than remaining a theoretical cryptographic exercise. The project's open tooling and documentation also make this research more accessible to engineers outside cryptography specialists.
- HEIR is Google's compiler for converting standard programs into ones that operate on encrypted data.
- The project can now compile small pre-trained ML models, such as a fraud-detection neural network, for private inference.
- Example code and setup instructions are publicly available via GitHub, requiring Bazel for installation.
Serious Cryptography, 2nd Edition (Jean-Philippe Aumasson) — If HEIR's promise of computing directly on encrypted data has you curious about the math underneath, this book is a great on-ramp — it covers modern cryptographic primitives in plain, practical language, including a look at fully homomorphic encryption. It's written for engineers who want intuition and real-world context rather than pure theory, which pairs perfectly with poking at a compiler like HEIR.
See Serious Cryptography, 2nd Edition (Jean-Philippe Aumasson) on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.Source: jeremykun.com, 2026-09-04
Published there as: “Updates on HEIR, the Homomorphic Encryption Compiler Project”
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