Abstract
k-anonymity is an approach for enabling privacy-preserving data publishing of personal, sensitive data. As a result of this anonymisation process, the utility of the sanitised data is generally lower than on the original data. Quantifying this utility loss is therefore important to estimate the usefulness of the resulting datasets. In this paper, we analyse several of these utility aspects.
Data utility can be measured as a direct property of the resulting, anonymised dataset, or via the effectiveness that a statistical analysis, such as a machine learning model, achieves upon this dataset, as compared to the original data. While the latter is more tailored to the specific dataset, it is also generally less efficient. We therefore analyse whether there is a correlation between these two types of measures, and whether the measurement on the effectiveness can be substituted by a measurement of the data properties. Further, we evaluate to what extent different solutions for the same level of k-anonymity differ in regards to effectiveness.
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Acknowledgements
This work was partially funded by the BRIDGE 1 programme (No 871267, “WellFort”) of the Austrian Research Promotion Agency (FFG), the EU Horizon 2020 research and innovation programme under grant agreement No. 826078 (Project “FeatureCloud”). SBA Research (SBA-K1) is funded within the framework of COMET—Competence Centers for Excellent Technologies by BMVIT, BMDW, and the federal state of Vienna, managed by the FFG.
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Appendix
Appendix
Classification results of the best, middle and worst found dataset of the solution space
Rankings for education
Rankings for marital-status
Utility metrics and loss correlation for education
Utility metrics and loss correlation for marital-status
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Šarčević, T., Molnar, D., Mayer, R. (2020). An Analysis of Different Notions of Effectiveness in k-Anonymity. In: Domingo-Ferrer, J., Muralidhar, K. (eds) Privacy in Statistical Databases. PSD 2020. Lecture Notes in Computer Science(), vol 12276. Springer, Cham. https://doi.org/10.1007/978-3-030-57521-2_9
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