Towards Split Learning-based Privacy-Preserving Record Linkage

Michail Zervas; Alexandros Karakasidis

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arXiv · 2409.01088

Towards Split Learning-based Privacy-Preserving Record Linkage

Michail Zervas·Alexandros Karakasidis

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Abstract

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.

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Michail Zervas, Alexandros Karakasidis. 2024-09-02. Towards Split Learning-based Privacy-Preserving Record Linkage. https://arxiv.org/abs/2409.01088

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