The task of calculating similarities between strings held by different
organizations without revealing these strings is an increasingly important
problem in areas such as health informatics, national censuses, genomics, and
fraud detection. Most existing privacy-preserving string comparison functions
are either based on comparing sets of encoded character q-grams, allow only
exact matching of encrypted strings, or they are aimed at long genomic
sequences that have a small alphabet. The set-based privacy-preserving
similarity functions commonly used to compare name and address strings in the
context of privacy-preserving record linkage do not take the positions of
sub-strings into account. As a result, two very different strings can
potentially be considered as an exact match leading to wrongly linked records.
Existing set-based techniques also cannot identify the length of the longest
common sub-string across two strings. In this paper we propose a novel approach
for accurate and efficient privacy-preserving string matching based on suffix
trees that are encoded using chained hashing. We incorporate a hashing based
encoding technique upon the encoded suffixes to improve privacy against
frequency attacks such as those exploiting Benford’s law. Our approach allows
various operations to be performed without the strings to be compared being
revealed: the length of the longest common sub-string, do two strings have the
same beginning, middle or end, and the longest common sub-string similarity
between two strings. These functions allow a more accurate comparison of, for
example, bank account, credit card, or telephone numbers, which cannot be
compared appropriately with existing privacy-preserving string matching
techniques. Our evaluation on several data sets with different types of strings
validates the privacy and accuracy of our proposed approach.

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