Abstract
In this paper we propose a method for statistical disclosure limitation of categorical variables that we call Conditional Group Swapping. This approach is suitable for design and strata-defining variables, the cross-classification of which leads to the formation of important groups or subpopulations. These groups are considered important because from the point of view of data analysis it is desirable to preserve analytical characteristics within them. In general data swapping can be quite distorting [13, 16, 20], especially for the relationships between the variables not only within the subpopulations but for the overall data. To reduce the damage incurred by swapping, we propose to choose the records for swapping using conditional probabilities which depend on the characteristics of the exchanged records. In particular, our approach exploits the results of propensity scores methodology for the computation of swapping probabilities. The experimental results presented in the paper show good utility properties of the method.
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Acknowledgments
The authors would like to thank Alan Dorfman and Van Parsons for valuable suggestions and help during the preparation of the paper. The findings and conclusions in this paper are those of of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
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Oganian, A., Lesaja, G. (2016). Propensity Score Based Conditional Group Swapping for Disclosure Limitation of Strata-Defining Variables. 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_6
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DOI: https://doi.org/10.1007/978-3-319-45381-1_6
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