A Survey of Privacy Attacks in Machine Learning

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.