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

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DLAW01 - 'Data linkage and anonymisation: setting the agenda'

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.


This talk is part of the Isaac Newton Institute Seminar Series series.

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