LinkIT: privacy preserving record linkage and integration via transformations

Authors:
Luca Bonomi;Li Xiong;James J. Lu
Affiliations:
Emory University, Atlanta, USA;Emory University, Atlanta, USA;Emory Uniersity, Atlanta, USA
Venue:
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Year:
2013

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Abstract

We propose to demonstrate an open-source tool, LinkIT, for privacy preserving record Linkage and Integration via data Transformations. LinkIT implements novel algorithms that support data transformations for linking sensitive attributes, and is designed to work with our previously developed tool, FRIL (Fine-grained Record Integration and Linkage), to provide a complete record linkage solution. LinkIT can be also used as a stand-alone secure transformation tool to link string records. The system uses a novel embedding technique based on frequent variable length grams mined from original records with differential privacy, and utilizes a personalized threshold for performing linkage in the embedded space. Compared to the state-of-the-art secure transformation method [16], LinkIT guarantees stronger privacy with better scalability while achieving comparable utility results.