Paper Info
Title
Privacy-Preserving Temporal Record Linkage
Abstract
Record linkage (RL) is the process of identifying matching records from different databases that refer to the same entity. It is common that the attribute values of records that belong to the same entity do evolve over time, for example people can change their surname or address. Therefore, to identify the records that refer to the same entity over time, RL should make use of temporal information such as the time-stamp of when a record was created and/or update last. However, if RL needs to be conducted on information about people, due to privacy and confidentiality concerns organizations are often not willing or allowed to share sensitive data in their databases, such as personal medical records, or location and financial details, with other organizations. This paper is the first to propose a privacy-preserving temporal record linkage (PPTRL) protocol that can link records across different databases while ensuring the privacy of the sensitive data in these databases. We propose a novel protocol based on Bloom filter encoding which incorporates the temporal information available in records during the linkage process. Our approach uses homomorphic encryption to securely calculate the probabilities of entities changing attribute values in their records over a period of time. Based on these probabilities we generate a set of masking Bloom filters to adjust the similarities between record pairs. We provide a theoretical analysis of the complexity and privacy of our technique and conduct an empirical study on large real databases containing several millions of records. The experimental results show that our approach can achieve better linkage quality compared to non-temporal PPRL while providing privacy to individuals in the databases that are being linked.
Year
DOI
Venue
2018
10.1109/ICDM.2018.00053
2018 IEEE International Conference on Data Mining (ICDM)
Keywords
Field
DocType
Homomorphic encryption,Bloom filters,Temporal Linkage
Homomorphic encryption,Record linkage,Data mining,Bloom filter,Confidentiality,Computer science,Medical record,Information privacy,Empirical research,Encoding (memory)
Conference
ISSN
ISBN
Citations 
1550-4786
978-1-5386-9160-1
0
PageRank 
References 
Authors
0.34
8
2
Name
Order
Citations
PageRank
Thilina Ranbaduge1123.64
Peter Christen21697107.21