  The process of data mining with differential privacy produces results that
are affected by two types of noise: sampling noise due to data collection and
privacy noise that is designed to prevent the reconstruction of sensitive
information. In this paper, we consider the problem of designing confidence
intervals for the parameters of a variety of differentially private machine
learning models. The algorithms can provide confidence intervals that satisfy
differential privacy (as well as the more recently proposed concentrated
differential privacy) and can be used with existing differentially private
mechanisms that train models using objective perturbation and output
perturbation.
