Google Cuts Homomorphic Encryption Overhead to 10-50x, Bringing Private AI Closer to Reality

Google has announced a significant advancement in homomorphic encryption (HE) that makes private AI inference more practically viable. The research focuses on optimizing HE for neural network matrix operations, developing TPU-based hardware acceleration, and combining HE with secure multi-party computation. These improvements reduce the computational overhead from roughly 1,000 times slower than standard processing down to approximately 10 to 50 times, crossing a key usability threshold. The breakthrough could enable sensitive sectors such as healthcare and finance to use cloud-based AI services without exposing underlying data. While overhead remains substantial and challenges persist for large models, the advance marks a notable step beyond prior work by Microsoft, IBM, and others in the HE space.