Google Open-Sources HEIR Compiler to Scale Private AI Inference

Google has open-sourced the Homomorphic Encryption Intermediate Representation (HEIR), a compiler toolchain designed to make private AI inference computationally practical. The project aims to bridge the gap between theoretical cryptography and production software engineering by automating how AI models handle encrypted data.

HEIR is an MLIR-based (Multi-Level Intermediate Representation) compiler that converts standard programs or pre-trained AI models into versions capable of operating directly on encrypted inputs. This enables "private AI inference," a process where servers process data and return results without ever accessing the cleartext information. By transforming input programs into versions that operate on encrypted data, HEIR removes the requirement for developers to be experts in cryptography to implement these systems.

The challenge of encrypted computation

Homomorphic encryption (HE) has long been a goal for data privacy, allowing computation on encrypted data without needing to decrypt it first. However, the technology has historically been too slow and complex for real-world production use. Implementing these schemes typically requires deep cryptographic expertise to manually convert algorithms into a format compatible with fully homomorphic encryption (FHE).

HEIR addresses these bottlenecks by providing a unified intermediate representation that abstracts the complexity of various FHE schemes. This allows the toolchain to target multiple backends, such as Lattigo and OpenFHE, while optimizing the resulting code for efficiency. To further combat the significant computational overhead associated with FHE, Google has partnered with hardware accelerator companies including Belfort, Niobium, Cornami, and Optalysys.

Industry implications and use cases

By lowering the barrier to entry for privacy-preserving AI, Google is positioning HE as a practical tool for highly regulated sectors. In industries such as healthcare and finance, where data privacy is paramount, the ability to process sensitive information without exposing it to the cloud provider could fundamentally change how AI is deployed.

Google has already demonstrated the utility of HEIR across several diverse applications. These include a Deep Learning Recommendation Model, a hotword detector, and the Kitsune anomaly detection system for network traffic. The toolchain has also been applied to credit card fraud detection, proving that encrypted inference can be applied to high-stakes security tasks.

The path to "one-click" privacy

Looking forward, the goal is to simplify the integration of encrypted inference to the point of invisibility for the end developer. The vision for HEIR is to create a "one-click solution" that enables non-experts to incorporate encrypted inference into production applications. As the toolchain matures and hardware acceleration improves, the industry will be watching to see if HEIR can reduce the performance penalty of FHE enough to make it a standard for all sensitive AI workloads.