Abstract
We discuss several methods for producing plots of uni- and bivariate distributions of confidential numeric microdata so that no single value is disclosed even in the presence of detailed additional knowledge, using the jackknife method of confidentiality protection. For histograms (as for frequency tables) this is similar to adding white noise of constant amplitude to all frequencies. Decreasing the bin size and smoothing, leading to kernel density estimation in the limit, gives more informative plots which need less noise for protection. Detail can be increased by choosing the bandwidth locally. Smoothing also the noise (i.e. using correlated noise) gives more visual improvement. Additional protection comes from robustifying the kernel density estimator or plotting only classified densities as in contour plots.
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Heitzig, J. (2006). Using the Jackknife Method to Produce Safe Plots of Microdata. In: Domingo-Ferrer, J., Franconi, L. (eds) Privacy in Statistical Databases. PSD 2006. Lecture Notes in Computer Science, vol 4302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11930242_13
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DOI: https://doi.org/10.1007/11930242_13
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