A Vulnerability Assessment Framework for Privacy-preserving Record Linkage

Abstract

The linkage of records to identify common entities across multiple data sources has gained increasing interest over the last few decades. In the absence of unique entity identifiers, quasi-identifying attributes such as personal names and addresses are generally used to link records. Due to privacy concerns that arise when such sensitive information is used, privacy-preserving record linkage (PPRL) methods have been proposed to link records without revealing any sensitive or confidential information about these records. Popular PPRL methods such as Bloom filter encoding, however, are known to be susceptible to various privacy attacks. Therefore, a systematic analysis of the privacy risks associated with sensitive databases as well as PPRL methods used in linkage projects is of great importance. In this article we present a novel framework to assess the vulnerabilities of sensitive databases and existing PPRL encoding methods. We discuss five types of vulnerabilities: frequency, length, co-occurrence, similarity, and similarity neighborhood, of both plaintext and encoded values that an adversary can exploit in order to reidentify sensitive plaintext values from encoded data. In an experimental evaluation we assess the vulnerabilities of two databases using five existing PPRL encoding methods. This evaluation shows that our proposed framework can be used in real-world linkage applications to assess the vulnerabilities associated with sensitive databases to be linked, as well as with PPRL encoding methods.