COCOA: A Synthetic Data Generator for Testing Anonymization Techniques

  • Vanessa Ayala-Rivera
  • A. Omar Portillo-Dominguez
  • Liam Murphy
  • Christina Thorpe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9867)


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.


Execution Time Attribute Generator Synthetic Data Garbage Collection Dataset Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported, in part, by Science Foundation Ireland grant 10/CE/I1855 to Lero - the Irish Software Research Centre (


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Vanessa Ayala-Rivera
    • 1
  • A. Omar Portillo-Dominguez
    • 1
  • Liam Murphy
    • 1
  • Christina Thorpe
    • 1
  1. 1.Lero@UCD, School of Computer ScienceUniversity College DublinDublinIreland

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