Clustering-Based Scalable Indexing for Multi-party Privacy-Preserving Record Linkage

  • Thilina Ranbaduge
  • Dinusha Vatsalan
  • Peter Christen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9078)

Abstract

The identification of common sets of records in multiple databases has become an increasingly important subject in many application areas, including banking, health, and national security. Often privacy concerns and regulations prevent the owners of the databases from sharing any sensitive details of their records with each other, and with any other party. The linkage of records in multiple databases while preserving privacy and confidentiality is an emerging research discipline known as privacy-preserving record linkage (PPRL). We propose a novel two-step indexing (blocking) approach for PPRL between multiple (more than two) parties. First, we generate small mini-blocks using a multi-bit Bloom filter splitting method and second we merge these mini-blocks based on their similarity using a novel hierarchical canopy clustering technique. An empirical study conducted with large datasets of up-to one million records shows that our approach is scalable with the size of the datasets and the number of parties, while providing better privacy than previous multi-party indexing approaches.

Keywords

Hierarchical canopy clustering Bloom filters Scalability 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Thilina Ranbaduge
    • 1
  • Dinusha Vatsalan
    • 1
  • Peter Christen
    • 1
  1. 1.Research School of Computer Science, College of Engineering and Computer ScienceThe Australian National UniversityCanberraAustralia

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