  Over the past few years, a tremendous growth of machine learning was brought
about by a significant increase in adoption of cloud-based services. As a
result, various solutions have been proposed in which the machine learning
models run on a remote cloud provider. However, when such a model is deployed
on an untrusted cloud, it is of vital importance that the users' privacy is
preserved. To this end, we propose Blind Faith -- a machine learning model in
which the training phase occurs in plaintext data, but the classification of
the users' inputs is performed on homomorphically encrypted ciphertexts. To
make our construction compatible with homomorphic encryption, we approximate
the activation functions using Chebyshev polynomials. This allowed us to build
a privacy-preserving machine learning model that can classify encrypted images.
Blind Faith preserves users' privacy since it can perform high accuracy
predictions by performing computations directly on encrypted data.
