Heidt Christian M, Hund Hauke, Fegeler Christian
GECKO Institute, Heilbronn University of Applied Sciences, Germany.
Stud Health Technol Inform. 2021 May 24;278:142-149. doi: 10.3233/SHTI210062.
The process of consolidating medical records from multiple institutions into one data set makes privacy-preserving record linkage (PPRL) a necessity. Most PPRL approaches, however, are only designed to link records from two institutions, and existing multi-party approaches tend to discard non-matching records, leading to incomplete result sets. In this paper, we propose a new algorithm for federated record linkage between multiple parties by a trusted third party using record-level bloom filters to preserve patient data privacy. We conduct a study to find optimal weights for linkage-relevant data fields and are able to achieve 99.5% linkage accuracy testing on the Febrl record linkage dataset. This approach is integrated into an end-to-end pseudonymization framework for medical data sharing.
将来自多个机构的医疗记录整合到一个数据集中的过程使得隐私保护记录链接（PPRL）成为必要。然而，大多数PPRL方法仅设计用于链接来自两个机构的记录，并且现有的多方方法往往会丢弃不匹配的记录，从而导致结果集不完整。在本文中，我们提出了一种新算法，用于由可信第三方通过使用记录级布隆过滤器来保护患者数据隐私，从而在多方之间进行联合记录链接。我们进行了一项研究，以找到与链接相关的数据字段的最佳权重，并且在Febrl记录链接数据集上进行测试时能够实现99.5%的链接准确率。这种方法被集成到一个用于医疗数据共享的端到端假名化框架中。
