More hybrid and secure protection of statistical data sets

Javier Herranz, Jordi Nin, Marc Solé

Research output: Indexed journal article Articlepeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Article number6189360
Pages (from-to)727-740
Number of pages14
JournalIEEE Transactions on Dependable and Secure Computing
Volume9
Issue number5
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Statistical data sets protection
  • hybrid methods
  • interval disclosure risk
  • synthetic methods

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