Hashing-Based Distributed Multi-party Blocking for Privacy-Preserving Record Linkage

Abstract

In many application domains organizations require information from multiple sources to be integrated. Due to privacy and confidentiality concerns often these organizations are not willing or allowed to reveal their sensitive and personal data to other database owners, and to any external party. This has led to the emerging research discipline of privacy-preserving record linkage (PPRL). We propose a novel blocking approach for multi-party PPRL to efficiently and effectively prune the record sets that are unlikely to match. Our approach allows each database owner to perform blocking independently except for the initial agreement of parameter settings and a final central hashing-based clustering. We provide an analysis of our technique in terms of complexity, quality, and privacy, and conduct an empirical study with large datasets. The results show that our approach is scalable with the size of the datasets and the number of parties, while providing better quality and privacy than previous multi-party private blocking approaches.