Jingchen (Monika) Hu, Associate Professor of Mathematics and Statistics, received support from the U.S. Department of Agriculture (USDA) and the Alfred P. Sloan Foundation to advance research, application, and outreach in the area of statistical data privacy. With the USDA's Economic Research Service (ERS), Monika is collaborating through a new cooperative agreement regarding social science data confidentiality. The project will explore issues that can surface when datasets are linked and help to inform ERS about best practices and new techniques to ensure data integrity.  Monika will explore the cascading impacts of having multiple publications featuring the same data, such as multiple journal articles using the same dataset, or year-to-year publications of estimates derived from the same data. She will also provide training and instruction on how best to evaluate risk and institute mitigation tools and strategies.  This research will expand the knowledge and research of disclosure risk evaluation and mitigation in new challenging settings, deepen Monika’s professional connections and collaborations with federal statistical agencies, and bring useful tools of disclosure risk evaluation and mitigation to statistical agencies for data protection.  This project will be of interest to other agencies engaging with confidential data, and can make a meaningful contribution to the field of disclosure risk and data protection in the interest of the public trust.

With her collaborators from the Institute for Computational and Data Sciences (ICDS) at The Pennsylvania State University, the National Institute of Statistical Sciences (NISS), and Urban Institute, Monika received a grant from the Alfred P. Sloan Foundation to support a series of workshops and webinars that will improve and strengthen the relationships among privacy experts, researchers, data stewards, data practitioners, and public policymakers. Their goal is to bridge the communication gap among the various groups and identify the tools and educational materials needed to improve expanding access to confidential demographic data for better public policy decision-making. Working groups formed from these workshops will result in a white paper for policymakers and a series of manuscripts suitable for journals about lessons learned and next steps confronting the data privacy and confidentiality community.  Composed of active researchers and advocates for statistical privacy and public policy, this collaborative group's year-long grant project promises to draw a large audience and make substantial impacts on the field.