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

arXiv: https://arxiv.org/abs/2309.10517 · HTML: https://mlsanity.com/paper/2309.10517/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?](https://mlsanity.com/paper/2003.12365/) (2020)
- [Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learning](https://mlsanity.com/paper/2301.08778/) (2023)
- [Blind Faith: Privacy-Preserving Machine Learning using Function Approximation](https://mlsanity.com/paper/2107.14338/) (2021)

## Built on by (most cited first, 8 of 8)

- [GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption](https://mlsanity.com/paper/2401.14840/) (2024)
- [Make Split, not Hijack: Preventing Feature-Space Hijacking Attacks in Split Learning](https://mlsanity.com/paper/2404.09265/) (2024)
- [A Taxonomy of Attacks and Defenses in Split Learning](https://mlsanity.com/paper/2505.05872/) (2025)
- [Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing](https://mlsanity.com/paper/2507.10494/) (2025)
- [A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption](https://mlsanity.com/paper/2507.14853/) (2025)
- [Verifiable Split Learning via zk-SNARKs](https://mlsanity.com/paper/2511.01356/) (2025)
- [Decentralized Privacy-Preserving Federal Learning of Computer Vision Models on Edge Devices](https://mlsanity.com/paper/2601.04912/) (2026)
- [SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacy](https://mlsanity.com/paper/2601.09460/) (2026)

## Abstract (arXiv)

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.