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February 17 @ 4:00 pm - 5:00 pm
Title: Valid Statistical Inference with Privacy Constraints
Presenter: Aleksandra Slavkovic
Abstract: Limiting the disclosure risk of sensitive data and statistical analyses is a long-standing problem in statistics. Differential privacy (DP), provides a framework for a strong provable privacy protection against arbitrary adversaries while allowing the release of summary statistics and potentially synthetic data. DP methods/mechanisms require the introduction of randomness which reduces the utility of the results especially in finite samples. In this talk we give an overview of statistical data privacy and its links to DP. We also describe a general framework, built on sound statistical principles from measurement error, robustness and the likelihood-based inference, and give specific examples of how to achieve optimal statistical inference under formal privacy, focused on survey and census data.