Towards Split Learning-based Privacy-Preserving Record Linkage

• URL: http://arxiv.org/abs/2409.01088v1
• Date: Mon, 2 Sep 2024 09:17:05 GMT
• Title: Towards Split Learning-based Privacy-Preserving Record Linkage
• Authors: Michail Zervas, Alexandros Karakasidis,
• Abstract summary: Split Learning has been introduced to facilitate applications where user data privacy is a requirement. In this paper, we investigate the potentials of Split Learning for Privacy-Preserving Record Matching.
• Score: 49.1574468325115
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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