More Hybrid and Secure Protection of Statistical Datasets

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 postprocessing 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.