  Commercial companies that collect user data on a large scale have been the
main beneficiaries of this trend since the success of deep learning techniques
is directly proportional to the amount of data available for training. Massive
data collection required for deep learning presents obvious privacy issues.
Users personal, highly sensitive data such as photos and voice recordings are
kept indefinitely by the companies that collect it. Users can neither delete it
nor restrict the purposes for which it is used. So, data privacy has been a
very important concern for governments and companies these days. It gives rise
to a very interesting challenge since on the one hand, we are pushing further
and further for high-quality models and accessible data, but on the other hand,
we need to keep data safe from both intentional and accidental leakage. The
more personal the data is it is more restricted it means some of the most
important social issues cannot be addressed using machine learning because
researchers do not have access to proper training data. But by learning how to
machine learning that protects privacy we can make a huge difference in solving
many social issues like curing disease etc. Deep neural networks are
susceptible to various inference attacks as they remember information about
their training data. In this chapter, we introduce differential privacy, which
ensures that different kinds of statistical analyses dont compromise privacy
and federated learning, training a machine learning model on a data to which we
do not have access to.
