Date: 2012-11-30

Time: 14:30-15:30

Location: BURN 1205

Abstract:

While statistical agencies would like to share their data with researchers, they must also protect the confidentiality of the data provided by their respondents. To satisfy these two conflicting objectives, agencies use various techniques to restrict and modify the data before publication. Most of these techniques however share a common flaw: their confidentiality protection can not be rigorously measured. In this talk, I will present the criterion of differential privacy, a rigorous measure of the protection offered by such methods. Designed to guarantee confidentiality even in a worst-case scenario, differential privacy protects the information of any individual in the database against an adversary with complete knowledge of the rest of the dataset. I will first give a brief overview of recent and current research on the topic of differential privacy. I will then focus on the publication of differentially-private synthetic contingency tables and present some of my results on the methods for the generation and proper analysis of such datasets.

Speaker

Anne-Sophie Charest is a newly hired Assistant Professor of Statistics at Université Laval, Québec. A McGill graduate, she recently completed her PhD at Carnegie Mellon University, Pittsburgh.