  In this paper, we study differentially private point and confidence interval
estimators for simple linear regression. Motivated by recent work that
highlights the strong empirical performance of an algorithm based on robust
statistics, DPTheilSen, we provide a rigorous, finite-sample analysis of its
privacy and accuracy properties, offer guidance on setting hyperparameters, and
show how to produce differentially private confidence intervals to accompany
its point estimates.
