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More Hybrid and Secure Protection of Statistical Data Sets

机译：统计数据集的更多混合和安全保护

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摘要

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.
机译：已经提出并研究了保护包含敏感统计信息的数据集的不同方法和范例。想法是发布不泄露机密信息但仍然允许用户获得有关原始数据的有意义的统计值的数据集的扰动版本。数据集保护的两个主要范例是经典形式和综合形式。近来，已经考虑了将两种范例结合而导致混合范例的可能性。在这项工作中，我们首先分析了最近几年提出的某些合成和（部分）混合方法的安全性，并得出结论，它们承受着较高的区间披露风险。然后，我们提出了第一个完全混合SDC方法。不幸的是，他们还遭受很高的区间披露风险。为了减轻这种情况，我们提出了一种后处理技术，该技术可应用于以合成方法保护的任何数据集，目的是降低其间隔披露风险。我们通过本文描述了在支持我们的主张的参考数据集上进行的一组实验。

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