A Survey of Privacy Attacks in Machine Learning
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by
Maria Rigaki, Sebastian Garcia
2020
Abstract
As machine learning becomes more widely used, the need to study its
implications in security and privacy becomes more urgent. Research on the
security aspects of machine learning, such as adversarial attacks, has received
a lot of focus and publicity, but privacy related attacks have received less
attention from the research community. Although there is a growing body of work
in the area, there is yet no extensive analysis of privacy related attacks. To
contribute into this research line we analyzed more than 40 papers related to
privacy attacks against machine learning that have been published during the
past seven years. Based on this analysis, an attack taxonomy is proposed
together with a threat model that allows the categorization of the different
attacks based on the adversarial knowledge and the assets under attack. In
addition, a detailed analysis of the different attacks is presented, including
the models under attack and the datasets used, as well as the common elements
and main differences between the approaches under the defined threat model.
Finally, we explore the potential reasons for privacy leaks and present an
overview of the most common proposed defenses.
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