 Disclosure RiskMatthias Templ ()
Chapter Chapter 3 in Statistical Disclosure Control for Microdata, 2017, pp 49-97 from Springer Abstract: 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. Keywords: Risk Measure; Simple Random Sampling; Frequency Count; Population Frequency; Inclusion Probability (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-50272-4_3 Ordering information: This item can be ordered from DOI: 10.1007/978-3-319-50272-4_3 Access Statistics for this chapter More chapters in Springer Books from Springer |
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