|Publication type:||Conference other|
|Type of review:||No review|
|Title:||Anonymization and re-identification risk of personal data|
|Conference details:||Gästekolloquium Psychologisches Institut Universität Zürich, Zürich, 9. November 2020|
|Subject (DDC):||005: Computer programming, programs and data|
|Abstract:||The demand for and volume of data from questionnaires/surveys, registers or other sources containing sensible information on persons or enterprises have been increased significantly over the last several years. At the same time, privacy protection principles and regulations have imposed restrictions on the access and use of individual data. Proper and secure microdata dissemination calls for the application of statistical disclosure control methods to data sets before release. Traditional approaches to (micro)data anonymization and statistical disclosure control include data perturbation methods, methods to quantify the disclosure risk, and methods to check the data utility of anonymized data sets. These traditional methods are enhanced by methods for the simulation of synthetic data sets. All methods should be able to deal with complex survey designs, missing values, hierarchical and cluster structures. In this colloquium lecture the topic of statistical disclosure control will be introduced to create awareness on this topic. The second part of the presentation discusses the state-of-the-art methods in selected topics on disclosure control and data anonymization."|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Data Analysis and Process Design (IDP)|
|Appears in collections:||Publikationen School of Engineering|
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