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

Differential Privacy and Machine Learning: a Survey and Review