Towards an End-to-End Approach to Formal Privacy for Sample Surveys - Publication

In this work, we design differentially private hypothesis tests for the following problems in the general linear model: testing a linear relationship and testing for the presence of mixtures. The majority of our hypothesis tests are based on...

The bootstrap is a common and powerful statistical tool for numerically computing the standard error of estimators, that is, a calculation of the uncertainty of functions computed on sample data so as to make an inference back to the original population...

There is a significant conceptual gap between legal and mathematica thinking around data privacy. The effect is uncertainty as to which technical offerings meet legal standards. This uncertainty is exacerbated by a litany of successful privacy attacks de...

Hypothesis testing plays a central role in statistical inference, and is used in many settings where privacy concerns are paramount. This work answers a basic question about privately testing simple hypotheses: given two distributions P and Q, and a...

We show a generic reduction from multiclass differentially private PAC learning to binary private PAC learning. We apply this transformation to a recently proposed binary private PAC learner to obtain a private multiclass learner with sample complexity...

 

There are significant gaps between legal and technical thinking around data privacy. Technical standards are described using mathematical language whereas legal standards are not rigorous from a mathematical point of view and often resort to concepts...

Government agencies always need to carefully consider potential risks of disclosure whenever they publish statistics based on their data or give external researchers access to the collected data. For this reason, research on disclosure avoiding techniques...

Over the past two decades, we have come to see that traditional de-anonymization techniques fail to protect the privacy of individuals in sensitive datasets. To address this problem, computer scientists introduced differential privacy, a strong...