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Disclosure Risk

In: Statistical Disclosure Control for Microdata

Author

Listed:
  • Matthias Templ

    (Zurich University of Applied Sciences (ZHAW), Institute of Data Analysis and Process Design (IDP), School of Engineering (SoE)
    data-analysis OG)

Abstract

One of the key tasks in SDC is to estimate the disclosure risk of individuals but also to estimate a global risk for the whole data set. A very basic idea is to calculate frequency counts of the categorical key variables. The concept of uniqueness and the concept of k-anonymity and l-diversity are important and outlined first. SUDA is extending the concept of k-anonymity it also searches for uniqueness in subsets of key variables. For surveys from complex designs, the estimation of frequency counts in the population and sample is of central interest. Mainly two approaches are used: the individual risk approach and the estimation of the global risk by log-linear models. For continuous key variables, other concepts are used to estimate the disclosure risk. They are rather based on distances than on counts. The risk estimation concepts presented here evaluate original data sets or data sets that are modified through traditional (perturbative) anonymization methods.

Suggested Citation

  • Matthias Templ, 2017. "Disclosure Risk," Springer Books, in: Statistical Disclosure Control for Microdata, chapter 0, pages 49-97, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-50272-4_3
    DOI: 10.1007/978-3-319-50272-4_3
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