Removing needles from the haystack: Challenges and strategies for de-identification in a world of open data
Data sharing has become more commonplace across disciplines, driven by funder and journal requirements, as well as growing desires for research to be more open and reproducible. This presents a challenge for data involving human participants, as openness and privacy are often at odds, and sharing can bring both benefit and risk. In this presentation, I will discuss challenges in sharing human data, including issues around appropriate consent, difficulties in de-identifying data for open access, and ethical considerations when sharing social media or other "public" data about humans. I will also discuss strategies for addressing these challenges, especially in the context of large scale computing and wide data availability.
Alicia Hofelich Mohr is the Research Support Coordinator in the Liberal Arts Technologies and Innovation Services (LATIS) at the University of Minnesota Twin Cities. Her research team provides consultation, workshops, and support for faculty and students across the research lifecycle, from data management planning through collection, analysis, sharing. Dr. Hofelich Mohr specializes in designing research workflows, handling human subjects data, de-identifying research data, and teaching reproducible data analysis in quantitative tools, such as R and SPSS. She has attended trainings on De-Identification Methodology from the Health Information Trust Alliance (HITRUST) and Data Management & Curation from the Inter-University Consortium for Political and Social Research (ICPSR). She serves as a curator for social science data and statistical code in the Data Repository for the University of Minnesota (DRUM). Dr. Hofelich Mohr holds a Ph.D. in Psychology and a M.A. in Statistics from the University of Michigan.