1 research outputs found
A Scalable Blocking Framework for Multidatabase Privacy-preserving Record Linkage
Today many application domains, such as national statistics,
healthcare, business analytic, fraud detection, and national
security, require data to be integrated from multiple databases.
Record linkage (RL) is a process used in data integration which
links multiple databases to identify matching records that belong
to the same entity. RL enriches the usefulness of data by
removing duplicates, errors, and inconsistencies which improves
the effectiveness of decision making in data analytic
applications.
Often, organisations are not willing or authorised to share the
sensitive information in their databases with any other party due
to privacy and confidentiality regulations. The linkage of
databases of different organisations is an emerging research area
known as privacy-preserving record linkage (PPRL). PPRL
facilitates the linkage of databases by ensuring the privacy of
the entities in these databases.
In multidatabase (MD) context, PPRL is significantly challenged
by the intrinsic exponential growth in the number of potential
record pair comparisons. Such linkage often requires significant
time and computational resources to produce the resulting
matching sets of records. Due to increased risk of collusion,
preserving the privacy of the data is more problematic with an
increase of number of parties involved in the linkage process.
Blocking is commonly used to scale the linkage of large
databases. The aim of blocking is to remove those record pairs
that correspond to non-matches (refer to different entities).
Many techniques have been proposed for RL and PPRL for blocking
two databases. However, many of these techniques are not suitable
for blocking multiple databases. This creates a need to develop
blocking technique for the multidatabase linkage context as
real-world applications increasingly require more than two
databases.
This thesis is the first to conduct extensive research on
blocking for multidatabase privacy-preserved record linkage
(MD-PPRL). We consider several research problems in blocking of
MD-PPRL. First, we start with a broad background literature on
PPRL. This allow us to identify the main research gaps that need
to be investigated in MD-PPRL. Second, we introduce a blocking
framework for MD-PPRL which provides more flexibility and control
to database owners in the block generation process. Third, we
propose different techniques that are used in our framework for
(1) blocking of multiple databases, (2) identifying blocks that
need to be compared across subgroups of these databases, and (3)
filtering redundant record pair comparisons by the efficient
scheduling of block comparisons to improve the scalability of
MD-PPRL. Each of these techniques covers an important aspect of
blocking in real-world MD-PPRL applications. Finally, this thesis
reports on an extensive evaluation of the combined application of
these methods with real datasets, which illustrates that they
outperform existing approaches in term of scalability, accuracy,
and privacy