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COCOA: A synthetic data generator for testing anonymization techniques
Ayala-Rivera, Vanessa; Portillo-Domínguez, Andrés Omar; Murphy, Liam; Thorpe, Christina
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 de ne 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.
Keyword(s): anonymization techniques; COCOA
Publication Date:
2016
Type: Conference item
Peer-Reviewed: Yes
Language(s): English
Institution: University of Limerick
Funder(s): Science Foundation Ireland
Citation(s): International Conference on Privacy in Statistical Databases: Lecture Notes in Computer Science (LNCS);9867, pp. 163-177
http://dx.doi.org/10.1007/978-3-319-45381-1_13
10/CE/I1855
Publisher(s): Springer
First Indexed: 2019-09-19 06:26:45 Last Updated: 2019-09-19 06:26:45