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Split learning (SL) is a new collaborative learning technique that allowsparticipants, e.g. a client and a server, to train machine learning modelswithout the client sharing raw data. In this setting, the client initiallyapplies its part of the machine learning model on the raw data to generateactivation maps and then sends them to the server to continue the trainingprocess. Previous works in the field demonstrated that reconstructingactivation maps could result in privacy leakage of client data. In addition tothat, existing mitigation techniques that overcome the privacy leakage of SLprove to be significantly worse in terms of accuracy. In this paper, we improveupon previous works by constructing a protocol based on U-shaped SL that canoperate on homomorphically encrypted data. More precisely, in our approach, theclient applies homomorphic encryption on the activation maps before sendingthem to the server, thus protecting user privacy. This is an importantimprovement that reduces privacy leakage in comparison to other SL-based works.Finally, our results show that, with the optimum set of parameters, trainingwith HE data in the U-shaped SL setting only reduces accuracy by 2.65% comparedto training on plaintext. In addition, raw training data privacy is preserved.

Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryption