This document discusses various methods to protect sensitive statistical data sets, highlighting classical, synthetic, and hybrid paradigms. It evaluates the security of existing synthetic and hybrid methods and reveals a high disclosure risk associated with them. The authors propose a new fully hybrid method alongside a post-processing technique to reduce this risk, supported by experimental results on reference data sets.

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More Hybrid and Secure Protection of Statistical Data Sets
Abstract—
Different methods and paradigms to protect data sets containing sensitive
statistical information have been proposed and studied. The idea is to publish a
perturbed version of the data set that does not leak confidential information, but
that still allows users to obtain meaningful statistical values about the original data.
The two main paradigms for data set protection are the classical one and the
synthetic one. Recently, the possibility of combining the two paradigms, leading to
a hybrid paradigm, has been considered. In this work, we first analyze the security
of some synthetic and (partially) hybrid methods that have been proposed in the
last years, and we conclude that they suffer from a high interval disclosure risk.
We then propose the first fully hybrid SDC methods; unfortunately, they also
suffer from a quite high interval disclosure risk. To mitigate this, we propose a post
processing technique that can be applied to any data set protected with a synthetic
method, with the goal of reducing its interval disclosure risk. We describe through
the paper a set of experiments performed on reference data sets that support our
claims..
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