TY - JOUR
T1 - On the disclosure risk of multivariate microaggregation
AU - Nin, Jordi
AU - Herranz, Javier
AU - Torra, Vicenç
N1 - Funding Information:
Jordi Nin (Barcelona, Catalonia, 1979; BSc 2004, MSc 2007, PhD 2008 all in Computer Science) is a post-doctoral researcher at the Artificial Intelligence Research Institute (IIIA-CSIC) near Barcelona, Catalonia, Spain. His fields of interests are privacy technologies, machine learning and soft computing tools. He has been involved in several research projects funded by the Catalan and Spanish governments and the European Community. His research has been published in specialized journals and major conferences (around 30 papers).
Funding Information:
Partial support by the Spanish MEC (projects ARES – CONSOLIDER INGENIO 2010 CSD2007-00004 – and eAEGIS – TSI2007-65406-C03-02) and by the Government of Catalonia (Grant 2005-SGR-00093) is acknowledged. Jordi Nin wants to thank the Spanish Council for Scientific Research (CSIC) for his I3P grant.
PY - 2008/12
Y1 - 2008/12
N2 - The aim of data protection methods is to protect a microdata file both minimizing the disclosure risk and preserving the data utility. Microaggregation is one of the most popular such methods among statistical agencies. Record linkage is the standard mechanism used to measure the disclosure risk of a microdata protection method. However, only standard, and quite generic, record linkage methods are usually considered, whereas more specific record linkage techniques can be more appropriate to evaluate the disclosure risk of some protection methods. In this paper we present a new record linkage technique, specific for microaggregation, which obtains more correct links than standard techniques. We have tested the new technique with MDAV microaggregation and two other microaggregation methods, based on projections, that we propose here for the first time. The direct consequence is that these microaggregation methods have a higher disclosure risk than believed up to now.
AB - The aim of data protection methods is to protect a microdata file both minimizing the disclosure risk and preserving the data utility. Microaggregation is one of the most popular such methods among statistical agencies. Record linkage is the standard mechanism used to measure the disclosure risk of a microdata protection method. However, only standard, and quite generic, record linkage methods are usually considered, whereas more specific record linkage techniques can be more appropriate to evaluate the disclosure risk of some protection methods. In this paper we present a new record linkage technique, specific for microaggregation, which obtains more correct links than standard techniques. We have tested the new technique with MDAV microaggregation and two other microaggregation methods, based on projections, that we propose here for the first time. The direct consequence is that these microaggregation methods have a higher disclosure risk than believed up to now.
KW - Data projection
KW - Disclosure risk
KW - Microaggregation
KW - Privacy in statistical databases
KW - Record linkage
UR - http://www.scopus.com/inward/record.url?scp=54349128838&partnerID=8YFLogxK
U2 - 10.1016/j.datak.2008.06.014
DO - 10.1016/j.datak.2008.06.014
M3 - Article
AN - SCOPUS:54349128838
SN - 0169-023X
VL - 67
SP - 399
EP - 412
JO - Data and Knowledge Engineering
JF - Data and Knowledge Engineering
IS - 3
ER -