Google Showcases HEIR for Private AI Inference With Homomorphic Encryption
Google has highlighted HEIR, an open-source compiler in its Private Computing Toolkit designed to make homomorphic-encryption-based private AI inference more practical.
Private AI without exposing raw data
Google has highlighted HEIR, an open-source compiler added to its Private Computing Toolkit, as a way to make privacy-preserving AI inference with homomorphic encryption more practical. The approach is designed for situations where a service needs to compute on sensitive information without receiving the underlying plaintext data.
That matters for sectors such as healthcare and finance, where data-sharing restrictions can limit the use of cloud AI even when useful models exist. Homomorphic encryption offers a different trade-off: computation can happen on encrypted inputs, reducing the need to reveal the original information to the service performing the inference.
Google's August 14 update positions HEIR as infrastructure for developers exploring this model of confidential computation. The technology is still specialized and performance-sensitive, so teams evaluating it should review Google's primary technical material and benchmark it against their own latency, security and deployment requirements.
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