# Towards Split Learning-based Privacy-Preserving Record Linkage

arXiv: https://arxiv.org/abs/2409.01088 · HTML: https://mlsanity.com/paper/2409.01088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 2 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Privacy-preserving Deep Learning based Record Linkage](https://mlsanity.com/paper/2211.02161/) (2022)

## Abstract (arXiv)

Split Learning has been recently introduced to facilitate applications where user data privacy is a requirement. However, it has not been thoroughly studied in the context of Privacy-Preserving Record Linkage, a problem in which the same real-world entity should be identified among databases from different dataholders, but without disclosing any additional information. In this paper, we investigate the potentials of Split Learning for Privacy-Preserving Record Matching, by introducing a novel training method through the utilization of Reference Sets, which are publicly available data corpora, showcasing minimal matching impact against a traditional centralized SVM-based technique.