Towards Split Learning-based Privacy-Preserving Record Linkage
This addresses privacy concerns in record linkage for dataholders, but it is incremental as it adapts an existing technique to a specific domain.
The paper tackles the problem of identifying matching records across databases without compromising privacy, using Split Learning with Reference Sets, achieving minimal performance impact compared to a centralized SVM method.
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.