A Privacy Attack on Multiple Dynamic Match-key based Privacy-Preserving Record Linkage

Over the last decade, the demand for linking records about people across databases has increased in various domains. Privacy challenges associated with linking sensitive information led to the development of privacy-preserving record linkage techniques. The multiple dynamic match-key encoding approach recently proposed by Randall et al. (IJPDS, 2019) is such a technique aimed at providing sufficient privacy for linkage applications while obtaining high linkage quality. However, the use of this encoding in large databases can reveal frequency information that can allow the re-identification of encoded values. Objectives We propose a frequency-based attack to evaluate the privacy guarantees of multiple dynamic match-key encoding. We then present two improvements to this match-key encoding approach to prevent such a privacy attack. Methods The proposed attack analyses the frequency distributions of individual match-keys in order to identify the attributes used for each match-key, where we assume the adversary has access to a plain-text database with similar characteristics as the encoded database. We employ a set of statistical correlation tests to compare the frequency distributions of match-key values between the encoded and plain-text databases. Once the attribute combinations used for match-keys are discovered, we then re-identify encoded sensitive values by utilising a frequency alignment method. Next, we propose two modifications to the match-key encoding; one to alter the original frequency distributions and another to make the frequency distributions uniform. Both will help to prevent frequency-based attacks. Results We evaluate our privacy attack using two large real-world databases. The results show that in certain situations the attack can successfully re-identify a set of sensitive values encoded using the multiple dynamic match-key encoding approach. On the databases used in our experiments, the attack is able to re-identify plain-text values with a precis