  This work provides a comprehensive review of existing frameworks based on
secure computing techniques in the context of private image classification. The
in-depth analysis of these approaches is followed by careful examination of
their performance costs, in particular runtime and communication overhead.
  To further illustrate the practical considerations when using different
privacy-preserving technologies, experiments were conducted using four
state-of-the-art libraries implementing secure computing at the heart of the
data science stack: PySyft and CrypTen supporting private inference via Secure
Multi-Party Computation, TF-Trusted utilising Trusted Execution Environments
and HE- Transformer relying on Homomorphic encryption.
  Our work aims to evaluate the suitability of these frameworks from a
usability, runtime requirements and accuracy point of view. In order to better
understand the gap between state-of-the-art protocols and what is currently
available in practice for a data scientist, we designed three neural network
architecture to obtain secure predictions via each of the four aforementioned
frameworks. Two networks were evaluated on the MNIST dataset and one on the
Malaria Cell image dataset. We observed satisfying performances for TF-Trusted
and CrypTen and noted that all frameworks perfectly preserved the accuracy of
the corresponding plaintext model.
