A Survey of Privacy Attacks in Machine Learning

Understand

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.