With access to supplementary datasets and computational power becoming more available, it is increasingly difficult to prevent re-identification of individuals in publicly released datasets. This course will provide an overview of current data privacy methodology, focusing on the generation of synthetic data and the application of differentially private methods. Through examinations of case studies and hands-on exercises, you will learn to apply data privacy techniques and evaluate the resulting disclosure risk and data utility. Attendees should have basic R programming experience.
Materials for each part can be found at the links below.
Below are links to supplementary exercises and content.