Frauke Kreuter is currently on leave from the University of Mannheim and will spend the Spring/
Here, you can see a video of Prof. Dr. Frauke Kreuter's presentation „Datenschutz (Data Privacy) in Surveys“, held at LMU Munich.
Frauke Kreuter is head of the IAB Statistical Methods group and Professor at the University of Mannheim. She also holds a Professorship in the Joint Program in Survey Methodology at the University of Maryland, USA since 2004. Furthermore, Frauke Kreuter is Co-Dicrector of the Mannheim Center for Data Science. Prior to her appointment at the University of Mannheim she held an S-Professorship at the Institute for Statistics at the Ludwig-Maximilians-Universität in Munich, Germany. She received her PhD from the University of Konstanz in 2001. From 2001-2004 Frauke Kreuter was part of UCLA Statistics Department. Her research focuses on sampling and measurement errors in complex surveys, and the use of paradata to improve survey processes and survey estimates. Since joining the IAB her research expanded to investigate the joint use of survey and administrative data, as well as other newly emerging data sources.
Why should social scientists start working with big data?
Frauke Kreuter discusses breaking down the barriers to entry that social scientists face when working with new computational methods and technology.
Book Demo - Big Data and Social Science Frauke Kreuter talks about her latest book, Big Data and Social Science: A Practical Guide to Methods and Tools.
This talk will provide a social survey statisticians perspective on (differential) privacy. It is structured in four parts. The first part covers typical social science research questions and data types used to answer such questions. The second part provides a survey of typical data analyses techniques and typical work streams social scientists engage in when analyzing data. The third part explains in more detail a typical data collection and how design decisions will be affected if the field moves forward with differential privacy. I will close with some thoughts about unanticipated side effects and some general questions related to the implementation of differential privacy.
Within the U.S. Federal Statistical System there is a common notion to transform statistical agencies from relying primarily on survey data to create statistics by combining survey and administrative data. This notion is shared by program agencies and based on recommendation provided by the Commission on Evidence-Based Policymaking, new legislation is now in place that promotes the access of administrative data and the sharing of such data across program agencies. In all policy documents proper protection of privacy is mentioned as a desired goal. This presentation will introduce a set of typical administrative data, describe its structure, size, types of variables, describe applications/
Data Privacy: From Foundations to Applications