Using mahalanobis distance-based record linkage for disclosure risk assessment

Authors:
Vicenç Torra;John M. Abowd;Josep Domingo-Ferrer
Affiliations:
IIIA-CSIC, Campus UAB, Bellaterra, Catalonia;Edmund Ezra Day Professor of Industrial and Labor Relations, Director, Cornell Institute for Social and Economic Research (CISER), Ithaca, NY;Dept. of Computer Engineering and Maths, Universitat Rovira i Virgili, Tarragona, Catalonia
Venue:
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Year:
2006

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Abstract

Distance-based record linkage (DBRL) is a common approach to empirically assessing the disclosure risk in SDC-protected microdata. Usually, the Euclidean distance is used. In this paper, we explore the potential advantages of using the Mahalanobis distance for DBRL. We illustrate our point for partially synthetic microdata and show that, in some cases, Mahalanobis DBRL can yield a very high re-identification percentage, far superior to the one offered by other record linkage methods.