Group Linkage

Abstract

Poor quality data is prevalent in databases due to a variety of reasons, including transcription errors, lack of standards for recording database fields, etc. To be able to query and integrate such data, considerable recent work has focused on the record linkage problem, i.e., determine if two entities represented as relational records are approximately the same. Often entities are represented as groups of relational records, rather than individual relational records, e.g., households in a census survey consist of a group of persons. We refer to the problem of determining if two entities represented as groups are approximately the same as group linkage.Intuitively, two groups can be linked to each other if (i) there is high enough similarity between "matching" pairs of individual records that constitute the two groups, and (ii) there is a large fraction of such matching record pairs. In this paper, we formalize this intuition and propose a group linkage measure based on bipartite graph matching. Given a data set consisting of a large number of groups, efficiently finding groups with a high group linkage similarity to an input query group requires quickly eliminating the many groups that are unlikely to be desired matches. To enable this task, we present simpler group similarity measures that can be used either during fast pre-processing steps or as approximations to our proposed group linkage measure. These measures can be easily instantiated using SQL, permitting our techniques to be implemented inside the database system itself. We experimentally validate the utility of our measures and techniques using a variety of real and synthetic data sets.

Privacy Preserving Group Linkage

“…Group linkage [19] extends the scenario to take groups of records into consideration. Group-wise similarity [19] is calculated based on record-level similarity. When exact matching is enforced at the record level, Jaccard similarity [15] is employed at the group level: similarity of two groups (R and S) is defined as: SIM (R, S) = |R ∩ S|/|R ∪ S|.…”

“…linked) if and only if SIM (R i , S j ) ≥ θ, where SIM () is an arbitrary group similarity function and θ is a pre-negotiated threshold. In this paper, we follow the original group linkage definition [19] to use Jaccard similarity [15] as the group-level similarity measurement (see Section 3.2 for details). In real-world applications, the number of elements in groups is usually small (e.g.…”

“…The record linkage or merge-purge problem has been intensively studied in database, data mining, and statistics communities [27,3]. Group linkage [19] extends the scenario to take groups of records into consideration. Group-wise similarity [19] is calculated based on record-level similarity.…”

“…Its goal is to identify related records that are associated with the same entity from multiple databases. When we extend the concept of "records" to "groups of records", it becomes the group linkage (GL) problem [19], which is to determine if two or more groups of records are associated with the same entity.…”

“…The problem of privacy-preserving record linkage is to find the intersection of records from two parties, while not revealing any private records to each other. Recently, group linkage has been introduced to measure the similarity of groups of records [19]. When we extend the traditional privacy-preserving record linkage methods to group linkage measurement, group membership privacy becomes vulnerable -record identity could be discovered from unlinked groups.…”