Bloom filter encoded identifiers are increasingly used for privacy preserving record linkage applications, because they allow for errors in encrypted identifiers. However, little research on the security of Bloom filters has been published so far. In this paper, we formalize a successful attack on Bloom filters composed of bigrams. It has previously been assumed in the literature that an attacker knows the global data set from which a sample is drawn. In contrast, we suppose that an attacker does not know this global data set. Instead, we assume the adversary knows a publicly available list of the most frequent attributes. The attack is based on subtle filtering and elementary statistical analysis of encrypted bigrams. The attack described in this paper can be used for the deciphering of a whole database instead of only a small subset of the most frequent names, as in previous research. We illustrate our proposed method with an attack on a database of encrypted surnames. Finally, we describe modifications of the Bloom filters for preventing similar attacks.

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Privacy-preserving record linkage with Bloom filters has become increasingly popular in medical applications, since Bloom filters allow for probabilistic linkage of sensitive personal data. However, since evidence indicates that Bloom filters lack sufficiently high security where strong security guarantees are required, several suggestions for their improvement have been made in literature. One of those improvements proposes the storage of several identifiers in one single Bloom filter. In this paper we present an automated cryptanalysis of this Bloom filter variant. The three steps of this procedure constitute our main contributions: (1) a new method for the detection of Bloom filter encrytions of bigrams (so-called atoms), (2) the use of an optimization algorithm for the assignment of atoms to bigrams, (3) the reconstruction of the original attribute values by linkage against bigram sets obtained from lists of frequent attribute values in the underlying population. To sum up, our attack provides the first convincing attack on Bloom filter encryptions of records built from more than one identifier.

Privacy-preserving record linkage with Bloom filters has become increasingly popular in medical applications, since Bloom filters allow for probabilistic linkage of sensitive personal data. However, since evidence indicates that Bloom filters lack sufficiently high security where strong security guarantees are required, several suggestions for their improvement have been made in literature. One of those improvements proposes the storage of several identifiers in one single Bloom filter. In this paper we present an automated cryptanalysis of this Bloom filter variant. The three steps of this procedure constitute our main contributions: (1) a new method for the detection of Bloom filter encrytions of bigrams (so-called atoms), (2) the use of an optimization algorithm for the assignment of atoms to bigrams, (3) the reconstruction of the original attribute values by linkage against bigram sets obtained from lists of frequent attribute values in the underlying population. To sum up, our attack provides the first convincing attack on Bloom filter encryptions of records built from more than one identifier.

No abstract