“Formal Privacy Protection for Data Products Combining Individual and Employer Frames”, Ashwin Machanavajjhala (Duke University), Samuel Haney (Duke University), Matthew Graham (U.S. Census Bureau), Mark Kutzbach (U.S. Census Bureau), Lars Vilhuber (Cornell University and U.S. Census Bureau), John Abowd (Cornell University and U.S. Census Bureau)
John Abowd will be giving two talks at the University of Nebraska-Lincoln, at the opening of the Central Plains Federal Statistical Research Data Center. The first talk is titled “Social Science Research in the Era of Restricted-Access Data”
John M. Abowd and Ian M. Schmutte : “The Advantages And Disadvantages Of Statistical Disclosure Limitation For Program Evaluation”
Abstract: This paper formalizes the manner in which statistical disclosure limitation (SDL) hinders empirical research in economics. We also highlight a hitherto unappreciated advantage of SDL, formal privacy models, and synthetic data systems: they can serve as a defense against model overfitting and falsediscovery bias. More specifically, a synthetic data validation system can – and we argue should – be used in conjunction with systems in which researchers register their research design ahead of analysis. The key insight is that privacyprotected data can be used for model development while minimizing risk of model overfitting. To demonstrate these points, we develop a model in which the statistical agency collects data from a population, but publishes a version in which the data that have been intentionally distorted by some SDL process. We say the SDL process is ignorable if inferences based on the published data are indistinguishable from inferences based on the unprotected data. SDL is rarely ignorable. If the researcher has knowledge of the SDL model, she can conduct an SDLaware analysis that explicitly corrects for the effects of SDL. If, as is often the case, if the SDL model is unknown, we describe circumstances under which SDL can still be learned.
Lars Vilhuber chairs session at JSM which includes multiple papers with NCRN contribution (presenter bolded, NCRN participants in red italics):
Robustness of Employer List Linking to Methodological Variation — Mark J. Kutzbach, U.S. Census Bureau ; Graton Gathright, U.S. Census Bureau ; Andrew Green, U.S. Census Bureau/Cornell University ; Kristin McCue, U.S. Census Bureau ; Holly Monti, U.S. Census Bureau ; Ann Rodgers, University of Michigan ; Lars Vilhuber, Cornell University ; Nada Wasi, University of Michigan ; Christopher Wignall, Amazon.com
Two Perspectives on Commuting and Workplace: A Microdata Comparison of Home-to-Work Flows Across Linked Survey and Administrative Files— Andrew Green, Cornell University/U.S. Census Bureau ; Mark J. Kutzbach, U.S. Census Bureau ; Lars Vilhuber, Cornell University
Developing Job Linkages for the Health and Retirement Study — Kristin McCue, U.S. Census Bureau ; John M. Abowd, U.S. Census Bureau/Cornell University ; Margaret Levenstein, University of Michigan ; Matthew Shapiro, University of Michigan ; Ann Rodgers, University of Michigan ; Nada Wasi, University of Michigan ; Dhiren Patki, University of Michigan
Lars Vilhuber speaks about “Disclosure Limitation and Confidentiality Protection in Linked Data” at the Center for Interuniversity Research and Analysis of Organizations‘s conference on “Facilitate the access to Quebec data: How and to what ends?” The conference is jointly organized with the Quebec inter-University Centre for Social Statistics (QICSS). The presentation relies on joint work with John M. Abowd and Ian M. Schmutte.