Efficient private record linkage of very large datasets

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Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption (2017, background): “Such an assumption would not stand many real-world applications where identifiers are not stable and/or recorded with errors [Schnell, 2013, Winkler, 2009], making entity matching a prerequisite for working with vertically partitioned in most realistic scenarios.”
Entity Resolution and Federated Learning get a Federated Resolution (2018, background): “Case studies report that exact matching can be very damaging when identifiers are not stable and error-prone: 25% of true matches would have been missed by exact matching in a census operation [26, 29].”