Paper Info
Title
Multi-Party Privacy-Preserving Record Linkage using Bloom Filters.
Abstract
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 partiesu0027 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.
Year
Venue
Field
2016
arXiv: Databases
National security,Data mining,Bloom filter,Record linkage,Adversary model,Computer security,Computer science,Voter registration,Information sensitivity,Database,Encoding (memory),Scalability
DocType
Volume
Citations 
Journal
abs/1612.08835
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Dinusha Vatsalan120919.57
Peter Christen21697107.21