Updates on HEIR, the Homomorphic Encryption Compiler Project (www.jeremykun.com)

The recent update on HEIR, Google's homomorphic encryption (HE) compiler, highlights its capability to compile pre-trained machine learning models, enabling perfectly private inference without exposing any cleartext data. This development is significant for the AI/ML community as it represents a step towards leveraging HE for sensitive applications where data privacy is a critical concern. The HEIR compiler executes models while maintaining encryption at all stages, ensuring that no intermediate values are revealed. Technical demonstrations showcased include a credit card fraud detection model, where the model was assessed using encrypted inputs. Although the inference took about 2 seconds—an approximate 4,000x slowdown compared to cleartext execution—improvements are expected as HEIR integrates with GPUs, potentially reducing latency significantly. The project roadmap also discusses future enhancements like automating model compatibility with HEIR, which currently requires manual annotation and conversion to an intermediate representation. This aligns with ongoing research and development efforts to optimize encrypted ML inference, positioning HEIR as a pivotal tool for advancing data privacy in AI applications.