CE_19C: Introduction to Data Privacy and Data Synthesis Techniques (Added Fee)

With an increasingly connected and surveilled world, high-quality datasets can be more easily constructed but also are more vulnerable to abuse than ever. Although collecting more and better data can provide great benefits to society, for example by furthering medical research or targeting public investments to help those most in need, data privacy concerns surface when that information can be de-anonymized and used maliciously. This full-day course will provide an overview of current data privacy methodology, focusing on the generation of synthetic data. 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.