Protecting Record Linkage Identifiers Using a Language Model for Patient Names.

Linking information across databases fosters new research in the medical sciences. Recent European privacy regulations recommend encrypting personal identifiers used for linking. Bloom filter based methods are an increasingly popular Record Linkage method. However, basic Bloom filter encodings are prone to cryptographic attacks. Therefore, hardening methods against these attacks are required. In this paper, a new method for such a hardening method for Privacy-preserving Record Linkage (PPRL) technique is presented. By using a Markov chain-based language model of bigrams of identifiers during the encryption, protection against attacks is increased. Based on real-world mortality data, we compare unencrypted and state of the art PPRL methods with the results of the proposed hardening method.