Privately evaluating sensitive population record linkage without ground truth data

Data containing personal information allow detailed studies in the health and social sciences, such as population-related analysis. However, such studies often require the linking of two or more databases because information about a person can be scattered across multiple data sources. To address this issue of data being scattered, researchers have been working on linking records across multiple data sources to identify records that refer to the same person, or the same group of individuals (known as group linkage) using quasi-identifiers such as names and addresses which can be missing, out of date or contain errors or variations, making record linkage a very challenging task. Record linkage applications often also lack ground truth data in the form of matching and non-matching record pairs, which challenges the assessment of the quality of linkage algorithms. Furthermore, when linkage is conducted on sensitive data, for example personal health records, due to privacy concerns ground truth can generally not be obtained using methods such as crowd sourcing. This study therefore aims to develop methods to assess the linkage quality of sensitive data by using publicly available data sets, such as census or voter data, in a privacy-preserving manner, with a focus on the group linkage problem. Assuming that distinct groups, such as siblings in a family, are identifiable in both the sensitive and public data sets, we develop a novel method to estimate linkage quality using public data by encoding information that is commonly available in both sensitive and public data sets into a common representation using Bloom filters. Comparing these Bloom filters then allows the estimation of linkage quality. An evaluation using a real sensitive birth data set and a public census data set from Scotland shows the effectiveness of our proposed method for quality estimation, which achieves a median correlation of 98% with linkage quality calculated based on ground truth data.