  In this paper, we consider the task of answering linear queries under the
constraint of differential privacy. This is a general and well-studied class of
queries that captures other commonly studied classes, including predicate
queries and histogram queries. We show that the accuracy to which a set of
linear queries can be answered is closely related to its fat-shattering
dimension, a property that characterizes the learnability of real-valued
functions in the agnostic-learning setting.
