Google is making private AI practical with homomorphic encryption
Google says its HEIR compiler can turn trained AI models into versions that run inference on encrypted inputs.
HEIR is an open-source toolchain for homomorphic encryption, aimed at making private inference usable without a team of cryptographers. Google frames the benefit as letting servers compute on ciphertext and return encrypted results without seeing the underlying data. The post shows four demos: recommendations, credit-card fraud detection, encrypted network anomaly detection, and hotword detection. Latency figures are described as single-threaded CPU results, with accelerator work promised later. HN · Frontpage AI's note
HEIR is an open-source toolchain for homomorphic encryption, aimed at making private inference usable without a team of cryptographers. Google frames the benefit as letting servers compute on ciphertext and return encrypted results without seeing the underlying data. The post shows four demos: recommendations, credit-card fraud detection, encrypted network anomaly detection, and hotword detection. Latency figures are described as single-threaded CPU results, with accelerator work promised later. HN · Frontpage AI's note
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