TY - GEN
T1 - COCOA
T2 - International Conference on Privacy in Statistical Databases, PSD 2016
AU - Ayala-Rivera, Vanessa
AU - Portillo-Dominguez, A. Omar
AU - Murphy, Liam
AU - Thorpe, Christina
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84987940192
U2 - 10.1007/978-3-319-45381-1_13
DO - 10.1007/978-3-319-45381-1_13
M3 - Conference contribution
AN - SCOPUS:84987940192
SN - 9783319453804
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 163
EP - 177
BT - Privacy in Statistical Databases - UNESCO Chair in Data Privacy International Conference, PSD 2016, Proceedings
A2 - Domingo-Ferrer, Josep
A2 - Pejić-Bach, Mirjana
PB - Springer Verlag
Y2 - 14 September 2016 through 16 September 2016
ER -