COCOA: A Synthetic Data Generator for Testing Anonymization Techniques
|Title:||COCOA: A Synthetic Data Generator for Testing Anonymization Techniques||Authors:||Ayala-Rivera, Vanessa
Portillo Dominguez, Andres Omar
Murphy, Liam, B.E.
|Permanent link:||http://hdl.handle.net/10197/8763||Date:||16-Sep-2016||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.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||Springer||Copyright (published version):||2017 Springer||Keywords:||Synthetic data; Anonymization; Testing; Data privacy||DOI:||10.1007/978-3-319-45381-1_13||Language:||en||Status of Item:||Peer reviewed||Is part of:||Domingo-Ferrer, J., Pejić-Bach, M. (eds.). Lecture Notes in Computer Science, volume 9867||Conference Details:||UNESCO Chair in Data Privacy, International Conference, PSD 2016, Dubrovnik, Croatia, September 14–16, 2016|
|Appears in Collections:||Computer Science Research Collection|
PEL Research Collection
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