  Privacy-preserving record linkage (PPRL), the problem of identifying records
that correspond to the same real-world entity across several data sources held
by different parties without revealing any sensitive information about these
records, is increasingly being required in many real-world application areas.
Examples range from public health surveillance to crime and fraud detection,
and national security. Various techniques have been developed to tackle the
problem of PPRL, with the majority of them considering linking data from only
two sources. However, in many real-world applications data from more than two
sources need to be linked. In this paper we propose a viable solution for
multi-party PPRL using two efficient privacy techniques: Bloom filter encoding
and distributed secure summation. Our proposed protocol efficiently identifies
matching sets of records held by all data sources that have a similarity above
a certain minimum threshold. While being efficient, our protocol is also secure
under the semi-honest adversary model in that no party can learn any sensitive
information about any other parties' data, but all parties learn which of their
records have a high similarity with records held by the other parties. We
evaluate our protocol on a large real voter registration database showing the
scalability, linkage quality, and privacy of our approach.
