Towards Split Learning-based Privacy-Preserving Record Linkage

TL;DR

This paper explores the use of Split Learning for privacy-preserving record linkage, introducing a novel training method with reference sets that maintains high matching accuracy while protecting data privacy.

Contribution

It presents a new Split Learning-based training approach utilizing reference sets, advancing privacy-preserving record linkage techniques.

Findings

Minimal matching impact compared to centralized SVM methods

Effective privacy preservation in record linkage

Potential for scalable privacy-aware data matching

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.