Towards Split Learning-based Privacy-Preserving Record Linkage
<|reference_start|>Towards Split Learning-based Privacy-Preserving Record Linkage: 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.<|reference_end|>
@article{zervas2024towards,
    title={Towards Split Learning-based Privacy-Preserving Record Linkage},
    author={Michail Zervas and Alexandros Karakasidis},
    journal={KDD Undergraduate Consortium 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01088},
    primaryClass={cs.CR cs.DB cs.LG}
}