This webinar is devoted to the mathematical and theoretical underpinnings of guaranteed data anonymization. Topics covered include an overview of identifiers and quasi-identifiers, an introduction to k-anonymity, a look at some cases where k-anonymity breaks down, and anonymization hierarchies. The presenter will describe a method to assess a survey dataset for anonymization using standard statistical software and consider the question of "anonymization overkill". Much of the academic material looking at data anonymization is quite abstract and aimed at computer scientists, while material aimed at data curators does not always consider recent developments. This webinar is intended to help bridge the gap.
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