Google released HEIR, an open‑source compiler that converts standard AI models to run on encrypted data, enabling private inference at scale.

Google has unveiled HEIR, an open‑source compiler that translates conventional AI models into versions capable of running on homomorphically encrypted data, making private inference feasible for large‑scale deployments.

How HEIR Bridges the Gap Between AI and Encryption

Homomorphic encryption (HE) lets data remain encrypted while it is being processed, but the computational overhead has traditionally limited its use in real‑time AI services. HEIR addresses this bottleneck by automating the conversion of TensorFlow and PyTorch models into HE‑compatible code, optimizing arithmetic circuits to reduce latency and memory consumption.

Key Features of the Compiler

  • Supports popular model formats including SavedModel and ONNX
  • Integrates with Google Cloud’s Confidential Computing environment
  • Provides automatic parameter selection for ciphertext modulus and scaling factors
  • Generates C++ and Rust back‑ends for deployment on diverse hardware

The tool also includes a benchmarking suite that measures inference time and accuracy loss after encryption, helping developers fine‑tune trade‑offs before production rollout.

Implications for Privacy‑Sensitive Applications

With HEIR, sectors such as healthcare, finance, and personalized advertising can run AI predictions on user data without ever exposing raw inputs, aligning with stricter data‑protection regulations like GDPR and HIPAA.

Google’s research team notes that the compiler’s open‑source nature encourages community contributions, potentially accelerating the adoption of homomorphic encryption across the broader AI ecosystem.

For a detailed overview, see the WebProNews coverage of Google’s HEIR compiler.