How Statisticians Should Grapple with Privacy in a Changing Data Landscape

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Acknowledgment

The authors thank Dr. Brian Vegetabile for providing helpful comments that greatly improved the quality of this article.

Additional information

Notes on contributors

Joshua Snoke

Joshua Snoke is an associate statistician at the RAND Corporation. His research focuses on statistical data privacy methods for increasing researchers’ access to data that are restricted due to privacy concerns. He has broad expertise in the field and has published work on various statistical data privacy topics, such as differential privacy, synthetic data, and privacy-preserving distributed estimation. He serves on the Privacy and Confidentiality Committee of the American Statistical Association and the RAND Human Subjects and Protections Committee. He received his PhD in statistics from the Pennsylvania State University.

Claire McKay Bowen

Claire McKay Bowen is the lead data scientist of privacy and data security at the Urban Institute. Her research focuses on comparing and evaluating the quality of differentially private data synthesis methods and science communication. After completing her PhD in Statistics from the University of Notre Dame, she worked at Los Alamos National Laboratory, where she investigated cosmic ray effects on supercomputers. She is also the recipient of the NSF Graduate Research Fellowship, Microsoft Graduate Women's Fellowship, and Gertrude M. Cox Scholarship.