How Researchers De-Identify Data in Practice

Human-subjects researchers are increasingly expected to de-identify and publish data about research participants. However, de-identification is difficult, lacking objective solutions for how to balance privacy and utility, and requiring significant time and expertise. To understand researchers' approaches, we interviewed 18 practitioners who have de-identified data for publication and 6 curators who review data submissions for repositories and funding organizations. We find that researchers account for the kinds of risks described by k-anonymity, but they address them through manual and social processes and not through systematic assessments of risk across a dataset. This allows for nuance but may leave published data vulnerable to re-identification. We explore why researchers take this approach and highlight three main barriers to more rigorous de-identification: threats seem unrealistic, stronger standards are not incentivized or supported, and tools do not meet researchers' needs. We conclude with takeaways for repositories, funding agencies, and privacy experts.