  As machine learning becomes more widely used, the need to study its
implications in security and privacy becomes more urgent. Although the body of
work in privacy has been steadily growing over the past few years, research on
the privacy aspects of machine learning has received less focus than the
security aspects. Our contribution in this research is an analysis of more than
40 papers related to privacy attacks against machine learning that have been
published during the past seven years. We propose an attack taxonomy, together
with a threat model that allows the categorization of different attacks based
on the adversarial knowledge, and the assets under attack. An initial
exploration of the causes of privacy leaks is presented, as well as a detailed
analysis of the different attacks. Finally, we present an overview of the most
commonly proposed defenses and a discussion of the open problems and future
directions identified during our analysis.
