Google Launches HEIR Compiler for AI Inference on Encrypted Data
The open-source toolchain allows AI models to process encrypted inputs, removing the trade-off between data utility and user privacy.
Google has introduced HEIR (Homomorphic Encryption Intermediate Representation), an open-source compiler toolchain designed to make private AI inference practical for developers. The system allows pre-trained AI models, which typically require unencrypted data, to be converted into versions capable of processing encrypted inputs.
By leveraging homomorphic encryption, HEIR enables servers to perform complex computations on data without ever decrypting it. This architecture ensures that sensitive user information remains encrypted throughout the entire inference process, effectively removing the traditional trade-off between the utility of AI services and the privacy of the underlying data. To demonstrate the toolchain's versatility, Google showcased four primary application demos: a credit card fraud detector, a hotword detector, a Deep Learning Recommendation Model, and a threat intrusion system known as Kitsune.
The Privacy Bottleneck
Traditional privacy methods, such as end-to-end encryption, often prevent service providers from offering data-dependent features like spam detection because the provider cannot see the data to analyze it. Conversely, moving AI processing entirely to local devices is often limited by hardware constraints and risks exposing a provider's proprietary model intellectual property. While homomorphic encryption has long been viewed as a solution to these problems, it has historically been hindered by significant computational overhead and latency, making it difficult to deploy in real-world production environments.
Scaling for Production
To address these performance hurdles, Google has partnered with several hardware accelerator companies, including Belfort, Niobium, Cornami, and Optalysys. These collaborations aim to reduce the latency overhead associated with homomorphic encryption, moving the technology closer to commercial viability. The development of HEIR also involved extensive academic collaboration with institutions including Carnegie Mellon, Georgia Tech, and Tsinghua University, a process that has already resulted in four peer-reviewed publications.
Industry Implications
If Google can successfully lower the barrier to entry through the HEIR compiler and specialized hardware acceleration, it could unlock widespread AI adoption in highly regulated sectors. In fields such as healthcare and finance, strict data privacy laws currently prevent the use of cloud-based AI on sensitive patient records or financial data. A practical, encrypted inference pipeline would allow these industries to utilize powerful cloud AI without violating compliance mandates or risking data exposure.
Next Steps
Google's Security and Research Team stated that their vision is to transform HEIR into a "one-click solution" that enables non-experts to integrate encrypted inference into production applications. For developers looking to experiment with the technology, Google has made the source code for the project's examples available in its fully-homomorphic-encryption GitHub repository.