Updates on HEIR, the Homomorphic Encryption Compiler Project
Google's HEIR project provides an honest, in-depth update on their homomorphic encryption compiler, detailing its progress in enabling private ML inference. This technical deep dive showcases current performance benchmarks and practical application heuristics, moving beyond corporate platitudes. HN readers appreciate the candid discussion of HE's current limitations and future roadmap in the quest for secure computation.
The Lowdown
This article offers a frank, jargon-rich update on HEIR, Google's open-source homomorphic encryption (HE) compiler project, diverging from a more constrained corporate blog post. HEIR's primary goal is to convert programs to operate directly on encrypted data, allowing for perfectly private inference of machine learning models without ever exposing cleartext information.
- Core Functionality: HEIR compiles pre-trained ML models, enabling operations on encrypted data with strong cryptographic guarantees, preventing any information leakage about inputs, outputs, or intermediate values.
- Performance Benchmarks: Initial single-threaded CPU benchmarks show significant slowdowns (e.g., 4,000x for a small fraud detection model compared to cleartext), but GPU acceleration dramatically reduces this to around 50x for more complex models, with further improvements expected from FPGAs and ASICs.
- Model Integration Challenges: Getting models into HEIR requires converting to MLIR (an intermediate representation) and manual annotations for secret inputs and activation function range bounds, though automation is being developed.
- Practical HE Applications & Constraints: The author identifies current suitable applications for HE based on several heuristics:
- Model Complexity: Best for smaller models (circa 2020 architectures) as larger transformers remain impractical.
- Dual Privacy Needs: Requires critical privacy for both user data and the service provider's model IP.
- User Count: Limited to applications with few simultaneous users due to massive key material storage and transfer overhead (tens to hundreds of GiB per user).
- Output Visibility: Applications where the server doesn't need to see the computation result, avoiding the complexity of zero-knowledge proofs.
- Examples: Remote diagnostics, B2B analysis, and specialized biometrics scenarios are suggested as promising use cases.
- HEIR's Roadmap: Future plans include integrating crypto directly into the compiler, expanding GPU and TPU support (including libraries like CHEDDAR and Belfort's), supporting new HE schemes (Gentry-Lee, Poulpy, Gao-Zheng), and developing robust benchmarking solutions through initiatives like fhe-benchmarking.org.
- Community & Research: HEIR is fully open source, actively engages with researchers, and has already contributed to several published papers, inviting new contributors via office hours and monthly meetings.
In essence, HEIR is a pivotal project pushing homomorphic encryption towards practicality. While significant performance and systems challenges remain, particularly in key management and scaling, the project demonstrates that private inference for certain ML applications is becoming viable, paving the way for a more privacy-preserving computational future.