Uniqueness and how it impacts privacy in health-related social science datasets

Abstract

Social scientists, like those performing research at the Kinsey Institute for Research in Sex, Gender and Reproduction, may use surveys to gather large amounts of sensitive data. Unlike purely medical-related datasets, these social science datasets tend to be sparse and high-dimensional, which presents opportunities to characterize participants in the dataset in unique ways. These unique characterizations may enable individuals to be linked to external data in ways that have not been previously considered. Therefore, traditional approaches to de-identifying data, such as fulfilling HIPAA requirements, may not be sufficient for preventing the re-identification of participants in large social science datasets.