Pushing the boundaries of homomorphic encryption
Programmable Bootstrapping

Zama uses a variant of TFHE that implements programmable bootstrapping (PBS), a breakthrough technique that allows any univariate function to be applied to the ciphertext while it is being bootstrapped. This enables a new FHE programming paradigm, where data flows (such as neural networks) are represented as efficient linear combinations of univariate functions instead of expensive boolean circuits.

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Zama is the only FHE solution that enables true homomorphic deep learning inference, using programmable bootstrapping to comnpute non-linear activation functions.

This benchmark shows the inference time for a single non-amortized inference, on networks having 20, 50 or 100 layers of 92 ReLu neurons, using 80 or 128 bits of security, running on AWS 96-core Xeon instances.

Programmable Bootstrapping Enables Efficient Homomorphic Inference of Deep Neural Networks
CONCRETE: Concrete Operates oN Ciphertexts Rapidly by Extending TfhE
Introduction to FHE (video)