De-Identifying Human Subject Data Techniques and Case Examples

Description

This class, lead by Johns Hopkins Data Services, builds upon the introduction to privacy protection class as a next step toward applying techniques for de-identifying several types of personal and health identifiers. Attendees will work through case examples and anonymization methods at an intermediate level suitable for preparing data for external collaborations and restricted access research databases. Attendees will also discuss the more common advanced statistical techniques for anonymization and risk assessment and their use cases as well as current software options. Prerequisite: De-identifying Human Subject Data for Sharing or reviewing Johns Hopkins Data Services' online resources.

Note: if you attended Johns Hopkins Data Services' de-identification webinar in prior years, reviewing the online module and guide can prepare you for this session.