Abstract
BackgroundData analysis for biomedical research often requires a record linkage step to identify records from multiple data sources referring to the same person. Due to the lack of unique personal identifiers across these sources, record linkage relies on the similarity of personal data such as first and last names or birth dates. However, the exchange of such identifying data with a third party, as is the case in record linkage, is generally subject to strict privacy requirements. This problem is addressed by privacy-preserving record linkage (PPRL) and pseudonymization services. Mainzelliste is an open-source record linkage and pseudonymization service used to carry out PPRL processes in real-world use cases.MethodsWe evaluate the linkage quality and performance of the linkage process using several real and near-real datasets with different properties w.r.t. size and error-rate of matching records. We conduct a comparison between (plaintext) record linkage and PPRL based on encoded records (Bloom filters). Furthermore, since the Mainzelliste software offers no blocking mechanism, we extend it by phonetic blocking as well as novel blocking schemes based on locality-sensitive hashing (LSH) to improve runtime for both standard and privacy-preserving record linkage.ResultsThe Mainzelliste achieves high linkage quality for PPRL using field-level Bloom filters due to the use of an error-tolerant matching algorithm that can handle variances in names, in particular missing or transposed name compounds. However, due to the absence of blocking, the runtimes are unacceptable for real use cases with larger datasets. The newly implemented blocking approaches improve runtimes by orders of magnitude while retaining high linkage quality.ConclusionWe conduct the first comprehensive evaluation of the record linkage facilities of the Mainzelliste software and extend it with blocking methods to improve its runtime. We observed a very high linkage quality for both plaintext as well as encoded data even in the presence of errors. The provided blocking methods provide order of magnitude improvements regarding runtime performance thus facilitating the use in research projects with large datasets and many participants.
Highlights
Data analysis for biomedical research often requires a record linkage step to identify records from multiple data sources referring to the same person
We comparatively evaluate record linkage based on original identifying data (IDAT) values against privacy-preserving record linkage (PPRL) on encoded IDAT (C-IDAT) using field-level Bloom filters
We propose two methods to apply locality-sensitive hashing (LSH) on a set of field-level Bloom filters {bv1, . . . , bvp} where p denotes the number of Bloom filters used for blocking
Summary
Data analysis for biomedical research often requires a record linkage step to identify records from multiple data sources referring to the same person. The exchange of such identifying data with a third party, as is the case in record linkage, is generally subject to strict privacy requirements This problem is addressed by privacy-preserving record linkage (PPRL) and pseudonymization services. Rohde et al J Transl Med (2021) 19:33 in the medical domain there are legal privacy requirements that generally do not allow to expose identifying data about patients to external parties thereby impeding the linkage of patient-related information. The latter challenge is addressed by privacy-preserving record linkage (PPRL) and pseudonymization techniques. The PID values allow to combine medical information about the same patient from multiple sources, e.g., within a research database, without revealing sensitive IDAT information
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