  The objective of machine learning is to extract useful information from data,
while privacy is preserved by concealing information. Thus it seems hard to
reconcile these competing interests. However, they frequently must be balanced
when mining sensitive data. For example, medical research represents an
important application where it is necessary both to extract useful information
and protect patient privacy. One way to resolve the conflict is to extract
general characteristics of whole populations without disclosing the private
information of individuals.
  In this paper, we consider differential privacy, one of the most popular and
powerful definitions of privacy. We explore the interplay between machine
learning and differential privacy, namely privacy-preserving machine learning
algorithms and learning-based data release mechanisms. We also describe some
theoretical results that address what can be learned differentially privately
and upper bounds of loss functions for differentially private algorithms.
  Finally, we present some open questions, including how to incorporate public
data, how to deal with missing data in private datasets, and whether, as the
number of observed samples grows arbitrarily large, differentially private
machine learning algorithms can be achieved at no cost to utility as compared
to corresponding non-differentially private algorithms.
