The Mathematics of Risk: An introduction to guaranteed data de-identification

Authors

Kristi Thompson, Western UniversityFollow

Document Type

Presentation

Publication Date

3-3-2022

Abstract

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.

Notes

Presented as part of the National Research Council's Responsible Data Speakers Series, 3 March 2022.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.