COCOA: A Synthetic Data Generator for Testing Anonymization Techniques

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Show simple item record Ayala-Rivera, Vanessa Portillo Dominguez, Andres Omar Murphy, Liam, B.E. Thorpe, Christina 2017-09-13T12:20:22Z 2017-09-13T12:20:22Z 2017 Springer en 2016-09-16
dc.description UNESCO Chair in Data Privacy, International Conference, PSD 2016, Dubrovnik, Croatia, September 14–16, 2016 en
dc.description.abstract Conducting extensive testing of anonymization techniques is critical to assess their robustness and identify the scenarios where they are most suitable. However, the access to real microdata is highly restricted and the one that is publicly-available is usually anonymized or aggregated; hence, reducing its value for testing purposes. In this paper, we present a framework (COCOA) for the generation of realistic synthetic microdata that allows to define multi-attribute relationships in order to preserve the functional dependencies of the data. We prove how COCOA is useful to strengthen the testing of anonymization techniques by broadening the number and diversity of the test scenarios. Results also show how COCOA is practical to generate large datasets. en
dc.description.sponsorship Science Foundation Ireland en
dc.language.iso en en
dc.publisher Springer en
dc.relation.ispartof Domingo-Ferrer, J., Pejić-Bach, M. (eds.). Lecture Notes in Computer Science, volume 9867 en
dc.rights The final publication is available at Springer via en
dc.subject Synthetic data en
dc.subject Anonymization en
dc.subject Testing en
dc.subject Data privacy en
dc.title COCOA: A Synthetic Data Generator for Testing Anonymization Techniques en
dc.type Conference Publication en
dc.status Peer reviewed en
dc.identifier.doi 10.1007/978-3-319-45381-1_13
dc.neeo.contributor Ayala-Rivera|Vanessa|aut|
dc.neeo.contributor Portillo Dominguez|Andres Omar|aut|
dc.neeo.contributor Murphy|Liam, B.E.|aut|
dc.neeo.contributor Thorpe|Christina|aut|
dc.internal.rmsid 788687916 2017-08-11T14:49:08Z

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