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.
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Acknowledgments
This work was supported, in part, by Science Foundation Ireland grant 10/CE/I1855 to Lero - the Irish Software Research Centre (www.lero.ie)
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Appendices
Appendix A PCA Constituent Metrics
Tables 1, 2, and 3 list the constituent metrics used to perform the PCA analysis of the datasets generated by COCOA (discussed in Sect. 4.2).
Appendix B Structure of the Irish census and insurance domains
Tables 4 and 5 list the attributes and the type of generators used for producing data for the Irish census and insurance domains, respectively (discussed in Sect. 3.5).
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Ayala-Rivera, V., Portillo-Dominguez, A.O., Murphy, L., Thorpe, C. (2016). COCOA: A Synthetic Data Generator for Testing Anonymization Techniques. In: Domingo-Ferrer, J., Pejić-Bach, M. (eds) Privacy in Statistical Databases. PSD 2016. Lecture Notes in Computer Science(), vol 9867. Springer, Cham. https://doi.org/10.1007/978-3-319-45381-1_13
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DOI: https://doi.org/10.1007/978-3-319-45381-1_13
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