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. How entities protect 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 offer an illustrative example to highlight potential disparities that can result from applying SDP methods. We develop an equitable statistical data privacy workflow that privacy practitioners and decisionmakers can utilize to explicitly make equity part of the standard data privacy process.