  Fully homomorphic encryption (FHE) is an encryption method that allows to
perform computation on encrypted data, without decryption. FHE preserves the
privacy of the users of online services that handle sensitive data, such as
health data, biometrics, credit scores and other personal information. A common
way to provide a valuable service on such data is through machine learning and,
at this time, Neural Networks are the dominant machine learning model for
unstructured data. In this work we show how to construct Deep Neural Networks
(DNN) that are compatible with the constraints of TFHE, an FHE scheme that
allows arbitrary depth computation circuits. We discuss the constraints and
show the architecture of DNNs for two computer vision tasks. We benchmark the
architectures using the Concrete stack, an open-source implementation of TFHE.

Machine Learning;Artificial Intelligence;Cryptography and Security