Love or Hate? Share or Split? Privacy-Preserving Training Using Split
  Learning and Homomorphic Encryption
Tanveer Khan Khoa Nguyen Antonis Michalas Alexandros Bakas
Split learning (SL) is a new collaborative learning technique that allows
participants, e.g. a client and a server, to train machine learning models
without the client sharing raw data. In this setting, the client initially
applies its part of the machine learning model on the raw data to generate
activation maps and then sends them to the server to continue the training
process. Previous works in the field demonstrated that reconstructing
activation maps could result in privacy leakage of client data. In addition to
that, existing mitigation techniques that overcome the privacy leakage of SL
prove to be significantly worse in terms of accuracy. In this paper, we improve
upon previous works by constructing a protocol based on U-shaped SL that can
operate on homomorphically encrypted data. More precisely, in our approach, the
client applies homomorphic encryption on the activation maps before sending
them to the server, thus protecting user privacy. This is an important
improvement that reduces privacy leakage in comparison to other SL-based works.
Finally, our results show that, with the optimum set of parameters, training
with HE data in the U-shaped SL setting only reduces accuracy by 2.65% compared
to training on plaintext. In addition, raw training data privacy is preserved.