  Over the past few years, a tremendous growth of machine learning was brought
about by a significant increase in adoption and implementation of cloud-based
services. As a result, various solutions have been proposed in which the
machine learning models run on a remote cloud provider and not locally on a
user's machine. However, when such a model is deployed on an untrusted cloud
provider, it is of vital importance that the users' privacy is preserved. To
this end, we propose Learning in the Dark -- a hybrid machine learning model in
which the training phase occurs in plaintext data, but the classification of
the users' inputs is performed directly on homomorphically encrypted
ciphertexts. To make our construction compatible with homomorphic encryption,
we approximate the ReLU and Sigmoid activation functions using low-degree
Chebyshev polynomials. This allowed us to build Learning in the Dark -- a
privacy-preserving machine learning model that can classify encrypted images
with high accuracy. Learning in the Dark preserves users' privacy since it is
capable of performing high accuracy predictions by performing computations
directly on encrypted data. In addition to that, the output of Learning in the
Dark is generated in a blind and therefore privacy-preserving way by utilizing
the properties of homomorphic encryption.
