Do No Harm Guide

Researchers and organizations can increase privacy in datasets through methods such as aggregating, suppressing, or substituting random values. But these means of protecting individuals' information do not always equally affect the groups of people represented in the data. A published dataset might ensure the privacy of people who make up the majority of the dataset but fail to ensure the privacy of those in smaller groups. Or, after undergoing alterations, the data may be more useful for learning about some groups more than others. Ultimately, how entities collect and share data can have varying effects on marginalized and underrepresented groups of people. To understand the current state of ideas, we completed a literature review of equity-focused work in statistical data privacy (SDP) and conducted interviews with nine experts on privacy-preserving methods and data sharing. These experts include researchers and practitioners from academia, government, and industry sectors with diverse technical backgrounds. We asked about their experience implementing data privacy and confidentiality methods and how they define equity in the context of privacy, among other topics. We also created an illustrative example to highlight potential disparities that can result from applying SDP methods without an equitable workflow.