Python-FHEz is a privacy-preserving Fully Homomorphic Encryption (FHE) and deep learning library.

Interactive graph example of an FHE compatible neural network:


Fully Homomorphic Encryption:

Fully Homomorphic Encryption stages: encoding, encryption, computation, decryption and decoding.

Deep Learning:

Example of artificial neural networks neuron, with inputs, parameters, and activations.

This library is capable of both fully homomorphically encrypting data and processing encrypted cyphertexts without the private keys, thus completely privately.

This library also supports:



  author = {George Onoufriou},
  title = {Python-FHEz Source Repository},
  year = {2021},
  url = {},

Or if you do not have @online support:

  author = {George Onoufriou},
  title = {Python-FHEz Source Repository},
  howpublished = {Github, GitLab},
  year = {2021},
  note = {\url{}},

Also See

This is part of a larger body of related together work. We are building similar tools for go (DarkLantern). We are also building fully open-source infastructure to run FHE using the server-client model, as well as offering this as a service to others (DeepCypher GitLab,