Abstract
Generating synthetic data for the dissemination of individual information in a privacy-preserving way is an approach that is often presented as superior to other statistical disclosure control techniques. The reason for such claim is straightforward at first glance: since all records disseminated are synthetic and not actual observed values, no individual can reasonably claim to face a privacy threat. Thus, and if the synthesizer used is good enough, synthetic data will potentially always offer a high level of information with low disclosure risk attached. Building on recent advances in the literature regarding the conceptualization of an intruder, this paper aims at challenging this claim by reassessing the privacy guarantees of synthetic data. Using the concept of a maximum-knowledge intruder, we demonstrate that synthetic data can in fact be always expressed as a re-arrangement of the original data and that, as a result, they may lead to configurations where disclosure risk may be higher than for non-synthetic disclosure control approaches. We illustrate the application of these results by an empirical example.
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Notes
- 1.
Using these notations, \( o_{ij} \) is the rank of attribute j in original record i and \( s_{lj}^{m} \) is the rank of attribute j in synthetic record l of the mth synthetic data set.
- 2.
The two other attributes are not shown here due to space constraints but their reverse-mapped versions can be displayed in exactly the same way.
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The following funding sources are gratefully acknowledged by the third author: European Commission (project H2020-700540 “CANVAS”), Government of Catalonia (ICREA Acadèmia Prize) and Spanish Government (projects TIN2014-57364-C2-1-R “SmartGlacis” and TIN2015-70054-REDC). The views in this paper are the authors’ own and do not necessarily reflect the views of UNESCO or any of the funders.
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Ruiz, N., Muralidhar, K., Domingo-Ferrer, J. (2018). On the Privacy Guarantees of Synthetic Data: A Reassessment from the Maximum-Knowledge Attacker Perspective. In: Domingo-Ferrer, J., Montes, F. (eds) Privacy in Statistical Databases. PSD 2018. Lecture Notes in Computer Science(), vol 11126. Springer, Cham. https://doi.org/10.1007/978-3-319-99771-1_5
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