Securing Bloom Filters for Privacy-preserving Record Linkage

Abstract

Privacy-preserving record linkage (PPRL) facilitates the matching of records that correspond to the same real-world entities across different databases while preserving the privacy of the individuals in these databases. A Bloom filter (BF) is a space efficient probabilistic data structure that is becoming popular in PPRL as an efficient privacy technique to encode sensitive information in records while still enabling approximate similarity computations between attribute values. However, BF encoding is susceptible to privacy attacks which can re-identify the values that are being encoded. In this paper we propose two novel techniques that can be applied on BF encoding to improve privacy against attacks. Our techniques use neighbouring bits in a BF to generate new bit values. An empirical study on large real databases shows that our techniques provide high security against privacy attacks, and achieve better similarity computation accuracy and linkage quality compared to other privacy improvements that can be applied on BF encoding.