Open AccessOvercoming the Impasse 2: Assessing the Quality of Recent Australian Applications of a Privacy-Preserving Record Linkage Method (PPRL-BLOOM)
Author(s) -
Sean Randall,
Adrian Brown,
Anna Ferrante,
James H. Boyd,
Katie Irvine,
Tom Eitelhuber,
Helen Wichmann
Publication year - 2020
Publication title -
international journal of population data science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.602
H-Index - 7
ISSN - 2399-4908
DOI - 10.23889/ijpds.v5i5.1489
Subject(s) - linkage (software) , record linkage , bloom filter , identifier , computer science , quality (philosophy) , data quality , personally identifiable information , data mining , internet privacy , data science , information retrieval , computer security , business , medicine , computer network , environmental health , biology , gene , genetics , population , philosophy , metric (unit) , epistemology , marketing
IntroductionWhile the quantity and type of datasets used by data linkage projects is growing, there remain some datasets that are ‘not available’ or ‘hard to access’ by researchers and linkers, either due to legal/regulatory constraints restricting the release of personally identifying information or because of privacy or reputational concerns. Advances in privacy-preserving record linkage methods (e.g. PPRL-Bloom) have made it possible to overcome this impasse. These techniques aim to provide strong privacy protection while still maintaining high linkage quality.
PPRL-Bloom methods are being used in practice. The Centre for Data Linkage (CDL) at Curtin University has been involved in several PPRL linkage and evaluation projects using real-world data. As the methods are relatively new, published information on achievable linkage quality in real-world scenarios is limited.
Objectives and ApproachWe present and describe several real-world applications of privacy preserving record linkage (PPRL-Bloom) where the quality of the linkage could be ascertained. In each case, data was linked ‘blind’; that is, without linkers having access to the original personal identifiers at any stage, or having any additional information about the records. Evaluations include a linkage of state-based morbidity and mortality records, a linkage of a number of general practice datasets to morbidity and emergency records, and a linkage of a range of state-based non-health administrative data, including education, police, housing, birth and child protection records.
ResultsThe privacy preserving record linkage performed admirably, with very high-quality results across all evaluations.
Conclusion / ImplicationsPrivacy preserving linkage is a useful and innovative methodology that is currently being used in real world projects. The results of these evaluation suggest it can be an appropriate linkage tool when legal or other constraints block release of personally identifying information to third party linkage units.