Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3233/SHTI210062 | GMDS |
Keywords | DocType | Volume |
Data Sharing,Pseudonyms,Record Linkage,Trusted Third Party | Conference | 278 |
ISSN | Citations | PageRank |
1879-8365 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian M Heidt | 1 | 0 | 0.68 |
| Hauke Hund | 2 | 0 | 1.35 |
| Christian Fegeler | 3 | 0 | 1.35 |