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Data Dissemination: A Survey of Recent Approaches, Challenges, and Connections to Data Linkage

Presented by: 
Jerry Reiter Duke University
Wednesday 6th July 2016 - 11:30 to 12:30
INI Seminar Room 1
I introduce common strategies for reducing disclosure risks when releasing public use microdata, i.e., data on individuals. I discuss some of their pros and cons in terms of data quality and disclosure risks, connecting to data linkage where possible. I also talk about a key challenge in data dissemination: how to give feedback to users on the quality of analyses of disclosure-protected data. Such feedback is essential if analysts are to trust results from (heavily) redacted microdata.  They also are essential for query systems that report (perturbed) outputs from statistical models. However, such feedback leaks information about confidential data values. I discuss approaches for feedback that satisfy the risk criterion differential privacy for releasing diagnostics in regression models.

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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons