A Sorted Neighborhood Approach to Multidimensional Privacy Preserving Blocking

Privacy Preserving Record Linkage is an emerging field of research which aims to integrate data from heterogeneous data sources while respecting privacy. It is evident that this task exhibits high computational complexity, therefore Privacy Preserving Blocking has been introduced in order to improve performance by eliminating unrelated candidate pairs. In this paper we present a solution to this problem by introducing the Sorted Neighborhood for Encrypted Fields algorithm and combining it with a secure multidimensional privacy preserving blocking method. Our approach is applicable to all types of data fields and manages to significantly boost the Privacy Preserving Record Linkage process without sacrificing matching accuracy. We analytically prove that our method is secure and we also provide empirical evidence where the high performance of our method is established by comparing it to other established methods.