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.

• Claire McKay Bowen, PhD ([email protected])
• Aaron R. Williams, MS ([email protected])
• Madeline Pickens, MS ([email protected])

• June 27, 2023: Introduction to Data Privacy and Synthetic Data
• July 11, 2023: Disclosure Risk and Utility Metrics and Synthetic Data Case Studies
• July 25, 2023: Introduction to Differential Privacy

Materials for each part can be found at the links below.

• Part 1: Introduction to Data Privacy
• Part 2: Disclosure Risk and Utility Metrics and Synthetic Data Case Studies
• Part 3: Introduction to Differential Privacy

Below are links to supplementary exercises and content.

• Bonus Discussion Exercises

• Demo of Data Synthesis in R

• Bonus Content: Advanced Differential Privacy